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faster_rcnn_inception_resnet_v2_keras_feature_extractor.py

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1# Copyright 2019 The TensorFlow Authors. All Rights Reserved.2#3# Licensed under the Apache License, Version 2.0 (the "License");4# you may not use this file except in compliance with the License.5# You may obtain a copy of the License at6#7# http://www.apache.org/licenses/LICENSE-2.08#9# Unless required by applicable law or agreed to in writing, software10# distributed under the License is distributed on an "AS IS" BASIS,11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.12# See the License for the specific language governing permissions and13# limitations under the License.14# ==============================================================================15"""Inception Resnet v2 Faster R-CNN implementation in Keras.16See "Inception-v4, Inception-ResNet and the Impact of Residual Connections on17Learning" by Szegedy et al. (https://arxiv.org/abs/1602.07261)18as well as19"Speed/accuracy trade-offs for modern convolutional object detectors" by20Huang et al. (https://arxiv.org/abs/1611.10012)21"""22# Skip pylint for this file because it times out23# pylint: skip-file24import tensorflow as tf25from object_detection.meta_architectures import faster_rcnn_meta_arch26from object_detection.models.keras_models import inception_resnet_v227from object_detection.utils import model_util28from object_detection.utils import variables_helper29class FasterRCNNInceptionResnetV2KerasFeatureExtractor(30 faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor):31 """Faster R-CNN with Inception Resnet v2 feature extractor implementation."""32 def __init__(self,33 is_training,34 first_stage_features_stride,35 batch_norm_trainable=False,36 weight_decay=0.0):37 """Constructor.38 Args:39 is_training: See base class.40 first_stage_features_stride: See base class.41 batch_norm_trainable: See base class.42 weight_decay: See base class.43 Raises:44 ValueError: If `first_stage_features_stride` is not 8 or 16.45 """46 if first_stage_features_stride != 8 and first_stage_features_stride != 16:47 raise ValueError('`first_stage_features_stride` must be 8 or 16.')48 super(FasterRCNNInceptionResnetV2KerasFeatureExtractor, self).__init__(49 is_training, first_stage_features_stride, batch_norm_trainable,50 weight_decay)51 def preprocess(self, resized_inputs):52 """Faster R-CNN with Inception Resnet v2 preprocessing.53 Maps pixel values to the range [-1, 1].54 Args:55 resized_inputs: A [batch, height_in, width_in, channels] float32 tensor56 representing a batch of images with values between 0 and 255.0.57 Returns:58 preprocessed_inputs: A [batch, height_out, width_out, channels] float3259 tensor representing a batch of images.60 """61 return (2.0 / 255.0) * resized_inputs - 1.062 def get_proposal_feature_extractor_model(self, name=None):63 """Returns a model that extracts first stage RPN features.64 Extracts features using the first half of the Inception Resnet v2 network.65 We construct the network in `align_feature_maps=True` mode, which means66 that all VALID paddings in the network are changed to SAME padding so that67 the feature maps are aligned.68 Args:69 name: A scope name to construct all variables within.70 Returns:71 A Keras model that takes preprocessed_inputs:72 A [batch, height, width, channels] float32 tensor73 representing a batch of images.74 And returns rpn_feature_map:75 A tensor with shape [batch, height, width, depth]76 """77 with tf.name_scope(name):78 with tf.name_scope('InceptionResnetV2'):79 model = inception_resnet_v2.inception_resnet_v2(80 self._train_batch_norm,81 output_stride=self._first_stage_features_stride,82 align_feature_maps=True,83 weight_decay=self._weight_decay,84 weights=None,85 include_top=False)86 proposal_features = model.get_layer(87 name='block17_20_ac').output88 return tf.keras.Model(89 inputs=model.inputs,90 outputs=proposal_features)91 def get_box_classifier_feature_extractor_model(self, name=None):92 """Returns a model that extracts second stage box classifier features.93 This function reconstructs the "second half" of the Inception ResNet v294 network after the part defined in `get_proposal_feature_extractor_model`.95 Args:96 name: A scope name to construct all variables within.97 Returns:98 A Keras model that takes proposal_feature_maps:99 A 4-D float tensor with shape100 [batch_size * self.max_num_proposals, crop_height, crop_width, depth]101 representing the feature map cropped to each proposal.102 And returns proposal_classifier_features:103 A 4-D float tensor with shape104 [batch_size * self.max_num_proposals, height, width, depth]105 representing box classifier features for each proposal.106 """107 with tf.name_scope(name):108 with tf.name_scope('InceptionResnetV2'):109 model = inception_resnet_v2.inception_resnet_v2(110 self._train_batch_norm,111 output_stride=16,112 align_feature_maps=False,113 weight_decay=self._weight_decay,114 weights=None,115 include_top=False)116 proposal_feature_maps = model.get_layer(117 name='block17_20_ac').output118 proposal_classifier_features = model.get_layer(119 name='conv_7b_ac').output120 return model_util.extract_submodel(121 model=model,122 inputs=proposal_feature_maps,123 outputs=proposal_classifier_features)124 def restore_from_classification_checkpoint_fn(125 self,126 first_stage_feature_extractor_scope,127 second_stage_feature_extractor_scope):128 """Returns a map of variables to load from a foreign checkpoint.129 This uses a hard-coded conversion to load into Keras from a slim-trained130 inception_resnet_v2 checkpoint.131 Note that this overrides the default implementation in132 faster_rcnn_meta_arch.FasterRCNNKerasFeatureExtractor which does not work133 for InceptionResnetV2 checkpoints.134 Args:135 first_stage_feature_extractor_scope: A scope name for the first stage136 feature extractor.137 second_stage_feature_extractor_scope: A scope name for the second stage138 feature extractor.139 Returns:140 A dict mapping variable names (to load from a checkpoint) to variables in141 the model graph.142 """143 keras_to_slim_name_mapping = {144 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d/kernel': 'InceptionResnetV2/Conv2d_1a_3x3/weights',145 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/beta': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/beta',146 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/moving_mean': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_mean',147 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm/moving_variance': 'InceptionResnetV2/Conv2d_1a_3x3/BatchNorm/moving_variance',148 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_1/kernel': 'InceptionResnetV2/Conv2d_2a_3x3/weights',149 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/beta': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/beta',150 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/moving_mean': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_mean',151 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_1/moving_variance': 'InceptionResnetV2/Conv2d_2a_3x3/BatchNorm/moving_variance',152 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_2/kernel': 'InceptionResnetV2/Conv2d_2b_3x3/weights',153 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/beta': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/beta',154 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/moving_mean': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_mean',155 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_2/moving_variance': 'InceptionResnetV2/Conv2d_2b_3x3/BatchNorm/moving_variance',156 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_3/kernel': 'InceptionResnetV2/Conv2d_3b_1x1/weights',157 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/beta': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/beta',158 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/moving_mean': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_mean',159 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_3/moving_variance': 'InceptionResnetV2/Conv2d_3b_1x1/BatchNorm/moving_variance',160 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_4/kernel': 'InceptionResnetV2/Conv2d_4a_3x3/weights',161 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/beta': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/beta',162 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/moving_mean': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_mean',163 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_4/moving_variance': 'InceptionResnetV2/Conv2d_4a_3x3/BatchNorm/moving_variance',164 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_5/kernel': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/weights',165 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/beta': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/beta',166 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',167 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_5/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',168 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_6/kernel': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/weights',169 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/beta': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',170 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',171 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_6/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',172 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_7/kernel': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/weights',173 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/beta': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/beta',174 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_mean',175 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_7/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_1/Conv2d_0b_5x5/BatchNorm/moving_variance',176 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_8/kernel': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/weights',177 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/beta': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',178 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',179 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_8/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',180 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_9/kernel': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/weights',181 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/beta': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',182 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',183 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_9/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',184 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_10/kernel': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/weights',185 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_10/beta': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',186 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_10/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',187 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_10/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',188 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_11/kernel': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/weights',189 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_11/beta': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/beta',190 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_11/moving_mean': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_mean',191 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_11/moving_variance': 'InceptionResnetV2/Mixed_5b/Branch_3/Conv2d_0b_1x1/BatchNorm/moving_variance',192 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_12/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/weights',193 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/beta',194 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',195 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_12/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',196 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_13/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/weights',197 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',198 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',199 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_13/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',200 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_14/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/weights',201 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',202 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',203 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_14/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',204 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_15/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/weights',205 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',206 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',207 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_15/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_variance',208 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_16/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/weights',209 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/beta',210 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_mean',211 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_16/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0b_3x3/BatchNorm/moving_variance',212 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_17/kernel': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/weights',213 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/beta': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/beta',214 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/moving_mean': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_mean',215 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_17/moving_variance': 'InceptionResnetV2/Repeat/block35_1/Branch_2/Conv2d_0c_3x3/BatchNorm/moving_variance',216 'FirstStageFeatureExtractor/InceptionResnetV2/block35_1_conv/kernel': 'InceptionResnetV2/Repeat/block35_1/Conv2d_1x1/weights',217 'FirstStageFeatureExtractor/InceptionResnetV2/block35_1_conv/bias': 'InceptionResnetV2/Repeat/block35_1/Conv2d_1x1/biases',218 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_18/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/weights',219 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/beta',220 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',221 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_18/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',222 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_19/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/weights',223 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',224 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',225 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_19/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',226 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_20/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/weights',227 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_20/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/beta',228 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_20/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_mean',229 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_20/moving_variance': 'InceptionResnetV2/Repeat/block35_2/Branch_1/Conv2d_0b_3x3/BatchNorm/moving_variance',230 'FirstStageFeatureExtractor/InceptionResnetV2/conv2d_21/kernel': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/weights',231 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_21/beta': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/beta',232 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_21/moving_mean': 'InceptionResnetV2/Repeat/block35_2/Branch_2/Conv2d_0a_1x1/BatchNorm/moving_mean',233 'FirstStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_21/moving_variance': 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'InceptionResnetV2/Repeat_2/block8_9/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',1016 'SecondStageFeatureExtractor/InceptionResnetV2/block8_9_conv/kernel': 'InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/weights',1017 'SecondStageFeatureExtractor/InceptionResnetV2/block8_9_conv/bias': 'InceptionResnetV2/Repeat_2/block8_9/Conv2d_1x1/biases',1018 'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_402/kernel': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/weights',1019 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/beta': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/beta',1020 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/moving_mean': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_mean',1021 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_402/moving_variance': 'InceptionResnetV2/Block8/Branch_0/Conv2d_1x1/BatchNorm/moving_variance',1022 'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_403/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/weights',1023 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/beta',1024 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_mean',1025 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_403/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0a_1x1/BatchNorm/moving_variance',1026 'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_404/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/weights',1027 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/beta',1028 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_mean',1029 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_404/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0b_1x3/BatchNorm/moving_variance',1030 'SecondStageFeatureExtractor/InceptionResnetV2/conv2d_405/kernel': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/weights',1031 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/beta': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/beta',1032 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/moving_mean': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_mean',1033 'SecondStageFeatureExtractor/InceptionResnetV2/freezable_batch_norm_405/moving_variance': 'InceptionResnetV2/Block8/Branch_1/Conv2d_0c_3x1/BatchNorm/moving_variance',1034 'SecondStageFeatureExtractor/InceptionResnetV2/block8_10_conv/kernel': 'InceptionResnetV2/Block8/Conv2d_1x1/weights',1035 'SecondStageFeatureExtractor/InceptionResnetV2/block8_10_conv/bias': 'InceptionResnetV2/Block8/Conv2d_1x1/biases',1036 'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b/kernel': 'InceptionResnetV2/Conv2d_7b_1x1/weights',1037 'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/beta': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/beta',1038 'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/moving_mean': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_mean',1039 'SecondStageFeatureExtractor/InceptionResnetV2/conv_7b_bn/moving_variance': 'InceptionResnetV2/Conv2d_7b_1x1/BatchNorm/moving_variance',1040 }1041 variables_to_restore = {}1042 for variable in variables_helper.get_global_variables_safely():1043 var_name = keras_to_slim_name_mapping.get(variable.op.name)1044 if var_name:1045 variables_to_restore[var_name] = variable...

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commctrl.py

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1# Generated by h2py from COMMCTRL.H2WM_USER = 10243ICC_LISTVIEW_CLASSES = 1 # listview, header4ICC_TREEVIEW_CLASSES = 2 # treeview, tooltips5ICC_BAR_CLASSES = 4 # toolbar, statusbar, trackbar, tooltips6ICC_TAB_CLASSES = 8 # tab, tooltips7ICC_UPDOWN_CLASS = 16 # updown8ICC_PROGRESS_CLASS = 32 # progress9ICC_HOTKEY_CLASS = 64 # hotkey10ICC_ANIMATE_CLASS = 128 # animate11ICC_WIN95_CLASSES = 25512ICC_DATE_CLASSES = 256 # month picker, date picker, time picker, updown13ICC_USEREX_CLASSES = 512 # comboex14ICC_COOL_CLASSES = 1024 # rebar (coolbar) control15ICC_INTERNET_CLASSES = 204816ICC_PAGESCROLLER_CLASS = 4096 # page scroller17ICC_NATIVEFNTCTL_CLASS = 8192 # native font control18ODT_HEADER = 10019ODT_TAB = 10120ODT_LISTVIEW = 10221PY_0U = 022NM_FIRST = (PY_0U) # generic to all controls23NM_LAST = (PY_0U- 99)24LVN_FIRST = (PY_0U-100) # listview25LVN_LAST = (PY_0U-199)26HDN_FIRST = (PY_0U-300) # header27HDN_LAST = (PY_0U-399)28TVN_FIRST = (PY_0U-400) # treeview29TVN_LAST = (PY_0U-499)30TTN_FIRST = (PY_0U-520) # tooltips31TTN_LAST = (PY_0U-549)32TCN_FIRST = (PY_0U-550) # tab control33TCN_LAST = (PY_0U-580)34CDN_FIRST = (PY_0U-601) # common dialog (new)35CDN_LAST = (PY_0U-699)36TBN_FIRST = (PY_0U-700) # toolbar37TBN_LAST = (PY_0U-720)38UDN_FIRST = (PY_0U-721) # updown39UDN_LAST = (PY_0U-740)40MCN_FIRST = (PY_0U-750) # monthcal41MCN_LAST = (PY_0U-759)42DTN_FIRST = (PY_0U-760) # datetimepick43DTN_LAST = (PY_0U-799)44CBEN_FIRST = (PY_0U-800) # combo box ex45CBEN_LAST = (PY_0U-830)46RBN_FIRST = (PY_0U-831) # rebar47RBN_LAST = (PY_0U-859)48IPN_FIRST = (PY_0U-860) # internet address49IPN_LAST = (PY_0U-879) # internet address50SBN_FIRST = (PY_0U-880) # status bar51SBN_LAST = (PY_0U-899)52PGN_FIRST = (PY_0U-900) # Pager Control53PGN_LAST = (PY_0U-950)54LVM_FIRST = 4096 # ListView messages55TV_FIRST = 4352 # TreeView messages56HDM_FIRST = 4608 # Header messages57TCM_FIRST = 4864 # Tab control messages58PGM_FIRST = 5120 # Pager control messages59CCM_FIRST = 8192 # Common control shared messages60CCM_SETBKCOLOR = (CCM_FIRST + 1) # lParam is bkColor61CCM_SETCOLORSCHEME = (CCM_FIRST + 2) # lParam is color scheme62CCM_GETCOLORSCHEME = (CCM_FIRST + 3) # fills in COLORSCHEME pointed to by lParam63CCM_GETDROPTARGET = (CCM_FIRST + 4)64CCM_SETUNICODEFORMAT = (CCM_FIRST + 5)65CCM_GETUNICODEFORMAT = (CCM_FIRST + 6)66INFOTIPSIZE = 102467NM_OUTOFMEMORY = (NM_FIRST-1)68NM_CLICK = (NM_FIRST-2) # uses NMCLICK struct69NM_DBLCLK = (NM_FIRST-3)70NM_RETURN = (NM_FIRST-4)71NM_RCLICK = (NM_FIRST-5) # uses NMCLICK struct72NM_RDBLCLK = (NM_FIRST-6)73NM_SETFOCUS = (NM_FIRST-7)74NM_KILLFOCUS = (NM_FIRST-8)75NM_CUSTOMDRAW = (NM_FIRST-12)76NM_HOVER = (NM_FIRST-13)77NM_NCHITTEST = (NM_FIRST-14) # uses NMMOUSE struct78NM_KEYDOWN = (NM_FIRST-15) # uses NMKEY struct79NM_RELEASEDCAPTURE = (NM_FIRST-16)80NM_SETCURSOR = (NM_FIRST-17) # uses NMMOUSE struct81NM_CHAR = (NM_FIRST-18) # uses NMCHAR struct82MSGF_COMMCTRL_BEGINDRAG = 1689683MSGF_COMMCTRL_SIZEHEADER = 1689784MSGF_COMMCTRL_DRAGSELECT = 1689885MSGF_COMMCTRL_TOOLBARCUST = 1689986CDRF_DODEFAULT = 087CDRF_NEWFONT = 288CDRF_SKIPDEFAULT = 489CDRF_NOTIFYPOSTPAINT = 1690CDRF_NOTIFYITEMDRAW = 3291CDRF_NOTIFYSUBITEMDRAW = 32 # flags are the same, we can distinguish by context92CDRF_NOTIFYPOSTERASE = 6493CDDS_PREPAINT = 194CDDS_POSTPAINT = 295CDDS_PREERASE = 396CDDS_POSTERASE = 497CDDS_ITEM = 6553698CDDS_ITEMPREPAINT = (CDDS_ITEM | CDDS_PREPAINT)99CDDS_ITEMPOSTPAINT = (CDDS_ITEM | CDDS_POSTPAINT)100CDDS_ITEMPREERASE = (CDDS_ITEM | CDDS_PREERASE)101CDDS_ITEMPOSTERASE = (CDDS_ITEM | CDDS_POSTERASE)102CDDS_SUBITEM = 131072103CDIS_SELECTED = 1104CDIS_GRAYED = 2105CDIS_DISABLED = 4106CDIS_CHECKED = 8107CDIS_FOCUS = 16108CDIS_DEFAULT = 32109CDIS_HOT = 64110CDIS_MARKED = 128111CDIS_INDETERMINATE = 256112CLR_NONE = -1 # 0xFFFFFFFFL113CLR_DEFAULT = -16777216 # 0xFF000000L114ILC_MASK = 1115ILC_COLOR = 0116ILC_COLORDDB = 254117ILC_COLOR4 = 4118ILC_COLOR8 = 8119ILC_COLOR16 = 16120ILC_COLOR24 = 24121ILC_COLOR32 = 32122ILC_PALETTE = 2048 # (not implemented)123ILD_NORMAL = 0124ILD_TRANSPARENT = 1125ILD_MASK = 16126ILD_IMAGE = 32127ILD_ROP = 64128ILD_BLEND25 = 2129ILD_BLEND50 = 4130ILD_OVERLAYMASK = 3840131ILD_SELECTED = ILD_BLEND50132ILD_FOCUS = ILD_BLEND25133ILD_BLEND = ILD_BLEND50134CLR_HILIGHT = CLR_DEFAULT135ILCF_MOVE = (0)136ILCF_SWAP = (1)137WC_HEADERA = "SysHeader32"138WC_HEADER = WC_HEADERA139HDS_HORZ = 0140HDS_BUTTONS = 2141HDS_HOTTRACK = 4142HDS_HIDDEN = 8143HDS_DRAGDROP = 64144HDS_FULLDRAG = 128145HDI_WIDTH = 1146HDI_HEIGHT = HDI_WIDTH147HDI_TEXT = 2148HDI_FORMAT = 4149HDI_LPARAM = 8150HDI_BITMAP = 16151HDI_IMAGE = 32152HDI_DI_SETITEM = 64153HDI_ORDER = 128154HDF_LEFT = 0155HDF_RIGHT = 1156HDF_CENTER = 2157HDF_JUSTIFYMASK = 3158HDF_RTLREADING = 4159HDF_OWNERDRAW = 32768160HDF_STRING = 16384161HDF_BITMAP = 8192162HDF_BITMAP_ON_RIGHT = 4096163HDF_IMAGE = 2048164HDM_GETITEMCOUNT = (HDM_FIRST + 0)165HDM_INSERTITEMA = (HDM_FIRST + 1)166HDM_INSERTITEMW = (HDM_FIRST + 10)167HDM_INSERTITEM = HDM_INSERTITEMA168HDM_DELETEITEM = (HDM_FIRST + 2)169HDM_GETITEMA = (HDM_FIRST + 3)170HDM_GETITEMW = (HDM_FIRST + 11)171HDM_GETITEM = HDM_GETITEMA172HDM_SETITEMA = (HDM_FIRST + 4)173HDM_SETITEMW = (HDM_FIRST + 12)174HDM_SETITEM = HDM_SETITEMA175HDM_LAYOUT = (HDM_FIRST + 5)176HHT_NOWHERE = 1177HHT_ONHEADER = 2178HHT_ONDIVIDER = 4179HHT_ONDIVOPEN = 8180HHT_ABOVE = 256181HHT_BELOW = 512182HHT_TORIGHT = 1024183HHT_TOLEFT = 2048184HDM_HITTEST = (HDM_FIRST + 6)185HDM_GETITEMRECT = (HDM_FIRST + 7)186HDM_SETIMAGELIST = (HDM_FIRST + 8)187HDM_GETIMAGELIST = (HDM_FIRST + 9)188HDM_ORDERTOINDEX = (HDM_FIRST + 15)189HDM_CREATEDRAGIMAGE = (HDM_FIRST + 16) # wparam = which item (by index)190HDM_GETORDERARRAY = (HDM_FIRST + 17)191HDM_SETORDERARRAY = (HDM_FIRST + 18)192HDM_SETHOTDIVIDER = (HDM_FIRST + 19)193HDM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT194HDM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT195HDN_ITEMCHANGINGA = (HDN_FIRST-0)196HDN_ITEMCHANGINGW = (HDN_FIRST-20)197HDN_ITEMCHANGEDA = (HDN_FIRST-1)198HDN_ITEMCHANGEDW = (HDN_FIRST-21)199HDN_ITEMCLICKA = (HDN_FIRST-2)200HDN_ITEMCLICKW = (HDN_FIRST-22)201HDN_ITEMDBLCLICKA = (HDN_FIRST-3)202HDN_ITEMDBLCLICKW = (HDN_FIRST-23)203HDN_DIVIDERDBLCLICKA = (HDN_FIRST-5)204HDN_DIVIDERDBLCLICKW = (HDN_FIRST-25)205HDN_BEGINTRACKA = (HDN_FIRST-6)206HDN_BEGINTRACKW = (HDN_FIRST-26)207HDN_ENDTRACKA = (HDN_FIRST-7)208HDN_ENDTRACKW = (HDN_FIRST-27)209HDN_TRACKA = (HDN_FIRST-8)210HDN_TRACKW = (HDN_FIRST-28)211HDN_GETDISPINFOA = (HDN_FIRST-9)212HDN_GETDISPINFOW = (HDN_FIRST-29)213HDN_BEGINDRAG = (HDN_FIRST-10)214HDN_ENDDRAG = (HDN_FIRST-11)215HDN_ITEMCHANGING = HDN_ITEMCHANGINGA216HDN_ITEMCHANGED = HDN_ITEMCHANGEDA217HDN_ITEMCLICK = HDN_ITEMCLICKA218HDN_ITEMDBLCLICK = HDN_ITEMDBLCLICKA219HDN_DIVIDERDBLCLICK = HDN_DIVIDERDBLCLICKA220HDN_BEGINTRACK = HDN_BEGINTRACKA221HDN_ENDTRACK = HDN_ENDTRACKA222HDN_TRACK = HDN_TRACKA223HDN_GETDISPINFO = HDN_GETDISPINFOA224TOOLBARCLASSNAMEA = "ToolbarWindow32"225TOOLBARCLASSNAME = TOOLBARCLASSNAMEA226CMB_MASKED = 2227TBSTATE_CHECKED = 1228TBSTATE_PRESSED = 2229TBSTATE_ENABLED = 4230TBSTATE_HIDDEN = 8231TBSTATE_INDETERMINATE = 16232TBSTATE_WRAP = 32233TBSTATE_ELLIPSES = 64234TBSTATE_MARKED = 128235TBSTYLE_BUTTON = 0236TBSTYLE_SEP = 1237TBSTYLE_CHECK = 2238TBSTYLE_GROUP = 4239TBSTYLE_CHECKGROUP = (TBSTYLE_GROUP | TBSTYLE_CHECK)240TBSTYLE_DROPDOWN = 8241TBSTYLE_AUTOSIZE = 16 # automatically calculate the cx of the button242TBSTYLE_NOPREFIX = 32 # if this button should not have accel prefix243TBSTYLE_TOOLTIPS = 256244TBSTYLE_WRAPABLE = 512245TBSTYLE_ALTDRAG = 1024246TBSTYLE_FLAT = 2048247TBSTYLE_LIST = 4096248TBSTYLE_CUSTOMERASE = 8192249TBSTYLE_REGISTERDROP = 16384250TBSTYLE_TRANSPARENT = 32768251TBSTYLE_EX_DRAWDDARROWS = 1252BTNS_BUTTON = TBSTYLE_BUTTON253BTNS_SEP = TBSTYLE_SEP # 0x0001254BTNS_CHECK = TBSTYLE_CHECK # 0x0002255BTNS_GROUP = TBSTYLE_GROUP # 0x0004256BTNS_CHECKGROUP = TBSTYLE_CHECKGROUP # (TBSTYLE_GROUP | TBSTYLE_CHECK)257BTNS_DROPDOWN = TBSTYLE_DROPDOWN # 0x0008258BTNS_AUTOSIZE = TBSTYLE_AUTOSIZE # 0x0010; automatically calculate the cx of the button259BTNS_NOPREFIX = TBSTYLE_NOPREFIX # 0x0020; this button should not have accel prefix260BTNS_SHOWTEXT = 64 # 0x0040 // ignored unless TBSTYLE_EX_MIXEDBUTTONS is set261BTNS_WHOLEDROPDOWN = 128 # 0x0080 // draw drop-down arrow, but without split arrow section262TBCDRF_NOEDGES = 65536 # Don't draw button edges263TBCDRF_HILITEHOTTRACK = 131072 # Use color of the button bk when hottracked264TBCDRF_NOOFFSET = 262144 # Don't offset button if pressed265TBCDRF_NOMARK = 524288 # Don't draw default highlight of image/text for TBSTATE_MARKED266TBCDRF_NOETCHEDEFFECT = 1048576 # Don't draw etched effect for disabled items267TB_ENABLEBUTTON = (WM_USER + 1)268TB_CHECKBUTTON = (WM_USER + 2)269TB_PRESSBUTTON = (WM_USER + 3)270TB_HIDEBUTTON = (WM_USER + 4)271TB_INDETERMINATE = (WM_USER + 5)272TB_MARKBUTTON = (WM_USER + 6)273TB_ISBUTTONENABLED = (WM_USER + 9)274TB_ISBUTTONCHECKED = (WM_USER + 10)275TB_ISBUTTONPRESSED = (WM_USER + 11)276TB_ISBUTTONHIDDEN = (WM_USER + 12)277TB_ISBUTTONINDETERMINATE = (WM_USER + 13)278TB_ISBUTTONHIGHLIGHTED = (WM_USER + 14)279TB_SETSTATE = (WM_USER + 17)280TB_GETSTATE = (WM_USER + 18)281TB_ADDBITMAP = (WM_USER + 19)282HINST_COMMCTRL = -1283IDB_STD_SMALL_COLOR = 0284IDB_STD_LARGE_COLOR = 1285IDB_VIEW_SMALL_COLOR = 4286IDB_VIEW_LARGE_COLOR = 5287IDB_HIST_SMALL_COLOR = 8288IDB_HIST_LARGE_COLOR = 9289STD_CUT = 0290STD_COPY = 1291STD_PASTE = 2292STD_UNDO = 3293STD_REDOW = 4294STD_DELETE = 5295STD_FILENEW = 6296STD_FILEOPEN = 7297STD_FILESAVE = 8298STD_PRINTPRE = 9299STD_PROPERTIES = 10300STD_HELP = 11301STD_FIND = 12302STD_REPLACE = 13303STD_PRINT = 14304VIEW_LARGEICONS = 0305VIEW_SMALLICONS = 1306VIEW_LIST = 2307VIEW_DETAILS = 3308VIEW_SORTNAME = 4309VIEW_SORTSIZE = 5310VIEW_SORTDATE = 6311VIEW_SORTTYPE = 7312VIEW_PARENTFOLDER = 8313VIEW_NETCONNECT = 9314VIEW_NETDISCONNECT = 10315VIEW_NEWFOLDER = 11316VIEW_VIEWMENU = 12317HIST_BACK = 0318HIST_FORWARD = 1319HIST_FAVORITES = 2320HIST_ADDTOFAVORITES = 3321HIST_VIEWTREE = 4322TB_ADDBUTTONSA = (WM_USER + 20)323TB_INSERTBUTTONA = (WM_USER + 21)324TB_ADDBUTTONS = (WM_USER + 20)325TB_INSERTBUTTON = (WM_USER + 21)326TB_DELETEBUTTON = (WM_USER + 22)327TB_GETBUTTON = (WM_USER + 23)328TB_BUTTONCOUNT = (WM_USER + 24)329TB_COMMANDTOINDEX = (WM_USER + 25)330TB_SAVERESTOREA = (WM_USER + 26)331TB_SAVERESTOREW = (WM_USER + 76)332TB_CUSTOMIZE = (WM_USER + 27)333TB_ADDSTRINGA = (WM_USER + 28)334TB_ADDSTRINGW = (WM_USER + 77)335TB_GETITEMRECT = (WM_USER + 29)336TB_BUTTONSTRUCTSIZE = (WM_USER + 30)337TB_SETBUTTONSIZE = (WM_USER + 31)338TB_SETBITMAPSIZE = (WM_USER + 32)339TB_AUTOSIZE = (WM_USER + 33)340TB_GETTOOLTIPS = (WM_USER + 35)341TB_SETTOOLTIPS = (WM_USER + 36)342TB_SETPARENT = (WM_USER + 37)343TB_SETROWS = (WM_USER + 39)344TB_GETROWS = (WM_USER + 40)345TB_SETCMDID = (WM_USER + 42)346TB_CHANGEBITMAP = (WM_USER + 43)347TB_GETBITMAP = (WM_USER + 44)348TB_GETBUTTONTEXTA = (WM_USER + 45)349TB_GETBUTTONTEXTW = (WM_USER + 75)350TB_REPLACEBITMAP = (WM_USER + 46)351TB_SETINDENT = (WM_USER + 47)352TB_SETIMAGELIST = (WM_USER + 48)353TB_GETIMAGELIST = (WM_USER + 49)354TB_LOADIMAGES = (WM_USER + 50)355TB_GETRECT = (WM_USER + 51) # wParam is the Cmd instead of index356TB_SETHOTIMAGELIST = (WM_USER + 52)357TB_GETHOTIMAGELIST = (WM_USER + 53)358TB_SETDISABLEDIMAGELIST = (WM_USER + 54)359TB_GETDISABLEDIMAGELIST = (WM_USER + 55)360TB_SETSTYLE = (WM_USER + 56)361TB_GETSTYLE = (WM_USER + 57)362TB_GETBUTTONSIZE = (WM_USER + 58)363TB_SETBUTTONWIDTH = (WM_USER + 59)364TB_SETMAXTEXTROWS = (WM_USER + 60)365TB_GETTEXTROWS = (WM_USER + 61)366TB_GETBUTTONTEXT = TB_GETBUTTONTEXTW367TB_SAVERESTORE = TB_SAVERESTOREW368TB_ADDSTRING = TB_ADDSTRINGW369TB_GETBUTTONTEXT = TB_GETBUTTONTEXTA370TB_SAVERESTORE = TB_SAVERESTOREA371TB_ADDSTRING = TB_ADDSTRINGA372TB_GETOBJECT = (WM_USER + 62) # wParam == IID, lParam void **ppv373TB_GETHOTITEM = (WM_USER + 71)374TB_SETHOTITEM = (WM_USER + 72) # wParam == iHotItem375TB_SETANCHORHIGHLIGHT = (WM_USER + 73) # wParam == TRUE/FALSE376TB_GETANCHORHIGHLIGHT = (WM_USER + 74)377TB_MAPACCELERATORA = (WM_USER + 78) # wParam == ch, lParam int * pidBtn378TBIMHT_AFTER = 1 # TRUE = insert After iButton, otherwise before379TBIMHT_BACKGROUND = 2 # TRUE iff missed buttons completely380TB_GETINSERTMARK = (WM_USER + 79) # lParam == LPTBINSERTMARK381TB_SETINSERTMARK = (WM_USER + 80) # lParam == LPTBINSERTMARK382TB_INSERTMARKHITTEST = (WM_USER + 81) # wParam == LPPOINT lParam == LPTBINSERTMARK383TB_MOVEBUTTON = (WM_USER + 82)384TB_GETMAXSIZE = (WM_USER + 83) # lParam == LPSIZE385TB_SETEXTENDEDSTYLE = (WM_USER + 84) # For TBSTYLE_EX_*386TB_GETEXTENDEDSTYLE = (WM_USER + 85) # For TBSTYLE_EX_*387TB_GETPADDING = (WM_USER + 86)388TB_SETPADDING = (WM_USER + 87)389TB_SETINSERTMARKCOLOR = (WM_USER + 88)390TB_GETINSERTMARKCOLOR = (WM_USER + 89)391TB_SETCOLORSCHEME = CCM_SETCOLORSCHEME # lParam is color scheme392TB_GETCOLORSCHEME = CCM_GETCOLORSCHEME # fills in COLORSCHEME pointed to by lParam393TB_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT394TB_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT395TB_MAPACCELERATORW = (WM_USER + 90) # wParam == ch, lParam int * pidBtn396TB_MAPACCELERATOR = TB_MAPACCELERATORW397TB_MAPACCELERATOR = TB_MAPACCELERATORA398TBBF_LARGE = 1399TB_GETBITMAPFLAGS = (WM_USER + 41)400TBIF_IMAGE = 1401TBIF_TEXT = 2402TBIF_STATE = 4403TBIF_STYLE = 8404TBIF_LPARAM = 16405TBIF_COMMAND = 32406TBIF_SIZE = 64407TB_GETBUTTONINFOW = (WM_USER + 63)408TB_SETBUTTONINFOW = (WM_USER + 64)409TB_GETBUTTONINFOA = (WM_USER + 65)410TB_SETBUTTONINFOA = (WM_USER + 66)411TB_INSERTBUTTONW = (WM_USER + 67)412TB_ADDBUTTONSW = (WM_USER + 68)413TB_HITTEST = (WM_USER + 69)414TB_SETDRAWTEXTFLAGS = (WM_USER + 70) # wParam == mask lParam == bit values415TBN_GETBUTTONINFOA = (TBN_FIRST-0)416TBN_GETBUTTONINFOW = (TBN_FIRST-20)417TBN_BEGINDRAG = (TBN_FIRST-1)418TBN_ENDDRAG = (TBN_FIRST-2)419TBN_BEGINADJUST = (TBN_FIRST-3)420TBN_ENDADJUST = (TBN_FIRST-4)421TBN_RESET = (TBN_FIRST-5)422TBN_QUERYINSERT = (TBN_FIRST-6)423TBN_QUERYDELETE = (TBN_FIRST-7)424TBN_TOOLBARCHANGE = (TBN_FIRST-8)425TBN_CUSTHELP = (TBN_FIRST-9)426TBN_DROPDOWN = (TBN_FIRST - 10)427TBN_GETOBJECT = (TBN_FIRST - 12)428HICF_OTHER = 0429HICF_MOUSE = 1 # Triggered by mouse430HICF_ARROWKEYS = 2 # Triggered by arrow keys431HICF_ACCELERATOR = 4 # Triggered by accelerator432HICF_DUPACCEL = 8 # This accelerator is not unique433HICF_ENTERING = 16 # idOld is invalid434HICF_LEAVING = 32 # idNew is invalid435HICF_RESELECT = 64 # hot item reselected436TBN_HOTITEMCHANGE = (TBN_FIRST - 13)437TBN_DRAGOUT = (TBN_FIRST - 14) # this is sent when the user clicks down on a button then drags off the button438TBN_DELETINGBUTTON = (TBN_FIRST - 15) # uses TBNOTIFY439TBN_GETDISPINFOA = (TBN_FIRST - 16) # This is sent when the toolbar needs some display information440TBN_GETDISPINFOW = (TBN_FIRST - 17) # This is sent when the toolbar needs some display information441TBN_GETINFOTIPA = (TBN_FIRST - 18)442TBN_GETINFOTIPW = (TBN_FIRST - 19)443TBN_GETINFOTIP = TBN_GETINFOTIPA444TBNF_IMAGE = 1445TBNF_TEXT = 2446TBNF_DI_SETITEM = 268435456447TBN_GETDISPINFO = TBN_GETDISPINFOA448TBDDRET_DEFAULT = 0449TBDDRET_NODEFAULT = 1450TBDDRET_TREATPRESSED = 2 # Treat as a standard press button451TBN_GETBUTTONINFO = TBN_GETBUTTONINFOA452REBARCLASSNAMEA = "ReBarWindow32"453REBARCLASSNAME = REBARCLASSNAMEA454RBIM_IMAGELIST = 1455RBS_TOOLTIPS = 256456RBS_VARHEIGHT = 512457RBS_BANDBORDERS = 1024458RBS_FIXEDORDER = 2048459RBS_REGISTERDROP = 4096460RBS_AUTOSIZE = 8192461RBS_VERTICALGRIPPER = 16384 # this always has the vertical gripper (default for horizontal mode)462RBS_DBLCLKTOGGLE = 32768463RBS_TOOLTIPS = 256464RBS_VARHEIGHT = 512465RBS_BANDBORDERS = 1024466RBS_FIXEDORDER = 2048467RBBS_BREAK = 1 # break to new line468RBBS_FIXEDSIZE = 2 # band can't be sized469RBBS_CHILDEDGE = 4 # edge around top & bottom of child window470RBBS_HIDDEN = 8 # don't show471RBBS_NOVERT = 16 # don't show when vertical472RBBS_FIXEDBMP = 32 # bitmap doesn't move during band resize473RBBS_VARIABLEHEIGHT = 64 # allow autosizing of this child vertically474RBBS_GRIPPERALWAYS = 128 # always show the gripper475RBBS_NOGRIPPER = 256 # never show the gripper476RBBIM_STYLE = 1477RBBIM_COLORS = 2478RBBIM_TEXT = 4479RBBIM_IMAGE = 8480RBBIM_CHILD = 16481RBBIM_CHILDSIZE = 32482RBBIM_SIZE = 64483RBBIM_BACKGROUND = 128484RBBIM_ID = 256485RBBIM_IDEALSIZE = 512486RBBIM_LPARAM = 1024487RB_INSERTBANDA = (WM_USER + 1)488RB_DELETEBAND = (WM_USER + 2)489RB_GETBARINFO = (WM_USER + 3)490RB_SETBARINFO = (WM_USER + 4)491RB_GETBANDINFO = (WM_USER + 5)492RB_SETBANDINFOA = (WM_USER + 6)493RB_SETPARENT = (WM_USER + 7)494RB_HITTEST = (WM_USER + 8)495RB_GETRECT = (WM_USER + 9)496RB_INSERTBANDW = (WM_USER + 10)497RB_SETBANDINFOW = (WM_USER + 11)498RB_GETBANDCOUNT = (WM_USER + 12)499RB_GETROWCOUNT = (WM_USER + 13)500RB_GETROWHEIGHT = (WM_USER + 14)501RB_IDTOINDEX = (WM_USER + 16) # wParam == id502RB_GETTOOLTIPS = (WM_USER + 17)503RB_SETTOOLTIPS = (WM_USER + 18)504RB_SETBKCOLOR = (WM_USER + 19) # sets the default BK color505RB_GETBKCOLOR = (WM_USER + 20) # defaults to CLR_NONE506RB_SETTEXTCOLOR = (WM_USER + 21)507RB_GETTEXTCOLOR = (WM_USER + 22) # defaults to 0x00000000508RB_SIZETORECT = (WM_USER + 23) # resize the rebar/break bands and such to this rect (lparam)509RB_SETCOLORSCHEME = CCM_SETCOLORSCHEME # lParam is color scheme510RB_GETCOLORSCHEME = CCM_GETCOLORSCHEME # fills in COLORSCHEME pointed to by lParam511RB_INSERTBAND = RB_INSERTBANDA512RB_SETBANDINFO = RB_SETBANDINFOA513RB_BEGINDRAG = (WM_USER + 24)514RB_ENDDRAG = (WM_USER + 25)515RB_DRAGMOVE = (WM_USER + 26)516RB_GETBARHEIGHT = (WM_USER + 27)517RB_GETBANDINFOW = (WM_USER + 28)518RB_GETBANDINFOA = (WM_USER + 29)519RB_GETBANDINFO = RB_GETBANDINFOA520RB_MINIMIZEBAND = (WM_USER + 30)521RB_MAXIMIZEBAND = (WM_USER + 31)522RB_GETDROPTARGET = (CCM_GETDROPTARGET)523RB_GETBANDBORDERS = (WM_USER + 34) # returns in lparam = lprc the amount of edges added to band wparam524RB_SHOWBAND = (WM_USER + 35) # show/hide band525RB_SETPALETTE = (WM_USER + 37)526RB_GETPALETTE = (WM_USER + 38)527RB_MOVEBAND = (WM_USER + 39)528RB_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT529RB_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT530RBN_HEIGHTCHANGE = (RBN_FIRST - 0)531RBN_GETOBJECT = (RBN_FIRST - 1)532RBN_LAYOUTCHANGED = (RBN_FIRST - 2)533RBN_AUTOSIZE = (RBN_FIRST - 3)534RBN_BEGINDRAG = (RBN_FIRST - 4)535RBN_ENDDRAG = (RBN_FIRST - 5)536RBN_DELETINGBAND = (RBN_FIRST - 6) # Uses NMREBAR537RBN_DELETEDBAND = (RBN_FIRST - 7) # Uses NMREBAR538RBN_CHILDSIZE = (RBN_FIRST - 8)539RBNM_ID = 1540RBNM_STYLE = 2541RBNM_LPARAM = 4542RBHT_NOWHERE = 1543RBHT_CAPTION = 2544RBHT_CLIENT = 3545RBHT_GRABBER = 4546TOOLTIPS_CLASSA = "tooltips_class32"547TOOLTIPS_CLASS = TOOLTIPS_CLASSA548TTS_ALWAYSTIP = 1549TTS_NOPREFIX = 2550TTF_IDISHWND = 1551TTF_CENTERTIP = 2552TTF_RTLREADING = 4553TTF_SUBCLASS = 16554TTF_TRACK = 32555TTF_ABSOLUTE = 128556TTF_TRANSPARENT = 256557TTF_DI_SETITEM = 32768 # valid only on the TTN_NEEDTEXT callback558TTDT_AUTOMATIC = 0559TTDT_RESHOW = 1560TTDT_AUTOPOP = 2561TTDT_INITIAL = 3562TTM_ACTIVATE = (WM_USER + 1)563TTM_SETDELAYTIME = (WM_USER + 3)564TTM_ADDTOOLA = (WM_USER + 4)565TTM_ADDTOOLW = (WM_USER + 50)566TTM_DELTOOLA = (WM_USER + 5)567TTM_DELTOOLW = (WM_USER + 51)568TTM_NEWTOOLRECTA = (WM_USER + 6)569TTM_NEWTOOLRECTW = (WM_USER + 52)570TTM_RELAYEVENT = (WM_USER + 7)571TTM_GETTOOLINFOA = (WM_USER + 8)572TTM_GETTOOLINFOW = (WM_USER + 53)573TTM_SETTOOLINFOA = (WM_USER + 9)574TTM_SETTOOLINFOW = (WM_USER + 54)575TTM_HITTESTA = (WM_USER +10)576TTM_HITTESTW = (WM_USER +55)577TTM_GETTEXTA = (WM_USER +11)578TTM_GETTEXTW = (WM_USER +56)579TTM_UPDATETIPTEXTA = (WM_USER +12)580TTM_UPDATETIPTEXTW = (WM_USER +57)581TTM_GETTOOLCOUNT = (WM_USER +13)582TTM_ENUMTOOLSA = (WM_USER +14)583TTM_ENUMTOOLSW = (WM_USER +58)584TTM_GETCURRENTTOOLA = (WM_USER + 15)585TTM_GETCURRENTTOOLW = (WM_USER + 59)586TTM_WINDOWFROMPOINT = (WM_USER + 16)587TTM_TRACKACTIVATE = (WM_USER + 17) # wParam = TRUE/FALSE start end lparam = LPTOOLINFO588TTM_TRACKPOSITION = (WM_USER + 18) # lParam = dwPos589TTM_SETTIPBKCOLOR = (WM_USER + 19)590TTM_SETTIPTEXTCOLOR = (WM_USER + 20)591TTM_GETDELAYTIME = (WM_USER + 21)592TTM_GETTIPBKCOLOR = (WM_USER + 22)593TTM_GETTIPTEXTCOLOR = (WM_USER + 23)594TTM_SETMAXTIPWIDTH = (WM_USER + 24)595TTM_GETMAXTIPWIDTH = (WM_USER + 25)596TTM_SETMARGIN = (WM_USER + 26) # lParam = lprc597TTM_GETMARGIN = (WM_USER + 27) # lParam = lprc598TTM_POP = (WM_USER + 28)599TTM_UPDATE = (WM_USER + 29)600TTM_ADDTOOL = TTM_ADDTOOLA601TTM_DELTOOL = TTM_DELTOOLA602TTM_NEWTOOLRECT = TTM_NEWTOOLRECTA603TTM_GETTOOLINFO = TTM_GETTOOLINFOA604TTM_SETTOOLINFO = TTM_SETTOOLINFOA605TTM_HITTEST = TTM_HITTESTA606TTM_GETTEXT = TTM_GETTEXTA607TTM_UPDATETIPTEXT = TTM_UPDATETIPTEXTA608TTM_ENUMTOOLS = TTM_ENUMTOOLSA609TTM_GETCURRENTTOOL = TTM_GETCURRENTTOOLA610TTN_GETDISPINFOA = (TTN_FIRST - 0)611TTN_GETDISPINFOW = (TTN_FIRST - 10)612TTN_SHOW = (TTN_FIRST - 1)613TTN_POP = (TTN_FIRST - 2)614TTN_GETDISPINFO = TTN_GETDISPINFOA615TTN_NEEDTEXT = TTN_GETDISPINFO616TTN_NEEDTEXTA = TTN_GETDISPINFOA617TTN_NEEDTEXTW = TTN_GETDISPINFOW618SBARS_SIZEGRIP = 256619SBARS_TOOLTIPS = 2048620STATUSCLASSNAMEA = "msctls_statusbar32"621STATUSCLASSNAME = STATUSCLASSNAMEA622SB_SETTEXTA = (WM_USER+1)623SB_SETTEXTW = (WM_USER+11)624SB_GETTEXTA = (WM_USER+2)625SB_GETTEXTW = (WM_USER+13)626SB_GETTEXTLENGTHA = (WM_USER+3)627SB_GETTEXTLENGTHW = (WM_USER+12)628SB_GETTEXT = SB_GETTEXTA629SB_SETTEXT = SB_SETTEXTA630SB_GETTEXTLENGTH = SB_GETTEXTLENGTHA631SB_SETPARTS = (WM_USER+4)632SB_GETPARTS = (WM_USER+6)633SB_GETBORDERS = (WM_USER+7)634SB_SETMINHEIGHT = (WM_USER+8)635SB_SIMPLE = (WM_USER+9)636SB_GETRECT = (WM_USER+10)637SB_ISSIMPLE = (WM_USER+14)638SB_SETICON = (WM_USER+15)639SB_SETTIPTEXTA = (WM_USER+16)640SB_SETTIPTEXTW = (WM_USER+17)641SB_GETTIPTEXTA = (WM_USER+18)642SB_GETTIPTEXTW = (WM_USER+19)643SB_GETICON = (WM_USER+20)644SB_SETTIPTEXT = SB_SETTIPTEXTA645SB_GETTIPTEXT = SB_GETTIPTEXTA646SB_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT647SB_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT648SBT_OWNERDRAW = 4096649SBT_NOBORDERS = 256650SBT_POPOUT = 512651SBT_RTLREADING = 1024652SBT_NOTABPARSING = 2048653SBT_TOOLTIPS = 2048654SB_SETBKCOLOR = CCM_SETBKCOLOR # lParam = bkColor655SBN_SIMPLEMODECHANGE = (SBN_FIRST - 0)656TRACKBAR_CLASSA = "msctls_trackbar32"657TRACKBAR_CLASS = TRACKBAR_CLASSA658TBS_AUTOTICKS = 1659TBS_VERT = 2660TBS_HORZ = 0661TBS_TOP = 4662TBS_BOTTOM = 0663TBS_LEFT = 4664TBS_RIGHT = 0665TBS_BOTH = 8666TBS_NOTICKS = 16667TBS_ENABLESELRANGE = 32668TBS_FIXEDLENGTH = 64669TBS_NOTHUMB = 128670TBS_TOOLTIPS = 256671TBM_GETPOS = (WM_USER)672TBM_GETRANGEMIN = (WM_USER+1)673TBM_GETRANGEMAX = (WM_USER+2)674TBM_GETTIC = (WM_USER+3)675TBM_SETTIC = (WM_USER+4)676TBM_SETPOS = (WM_USER+5)677TBM_SETRANGE = (WM_USER+6)678TBM_SETRANGEMIN = (WM_USER+7)679TBM_SETRANGEMAX = (WM_USER+8)680TBM_CLEARTICS = (WM_USER+9)681TBM_SETSEL = (WM_USER+10)682TBM_SETSELSTART = (WM_USER+11)683TBM_SETSELEND = (WM_USER+12)684TBM_GETPTICS = (WM_USER+14)685TBM_GETTICPOS = (WM_USER+15)686TBM_GETNUMTICS = (WM_USER+16)687TBM_GETSELSTART = (WM_USER+17)688TBM_GETSELEND = (WM_USER+18)689TBM_CLEARSEL = (WM_USER+19)690TBM_SETTICFREQ = (WM_USER+20)691TBM_SETPAGESIZE = (WM_USER+21)692TBM_GETPAGESIZE = (WM_USER+22)693TBM_SETLINESIZE = (WM_USER+23)694TBM_GETLINESIZE = (WM_USER+24)695TBM_GETTHUMBRECT = (WM_USER+25)696TBM_GETCHANNELRECT = (WM_USER+26)697TBM_SETTHUMBLENGTH = (WM_USER+27)698TBM_GETTHUMBLENGTH = (WM_USER+28)699TBM_SETTOOLTIPS = (WM_USER+29)700TBM_GETTOOLTIPS = (WM_USER+30)701TBM_SETTIPSIDE = (WM_USER+31)702TBTS_TOP = 0703TBTS_LEFT = 1704TBTS_BOTTOM = 2705TBTS_RIGHT = 3706TBM_SETBUDDY = (WM_USER+32) # wparam = BOOL fLeft; (or right)707TBM_GETBUDDY = (WM_USER+33) # wparam = BOOL fLeft; (or right)708TBM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT709TBM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT710TB_LINEUP = 0711TB_LINEDOWN = 1712TB_PAGEUP = 2713TB_PAGEDOWN = 3714TB_THUMBPOSITION = 4715TB_THUMBTRACK = 5716TB_TOP = 6717TB_BOTTOM = 7718TB_ENDTRACK = 8719TBCD_TICS = 1720TBCD_THUMB = 2721TBCD_CHANNEL = 3722DL_BEGINDRAG = (WM_USER+133)723DL_DRAGGING = (WM_USER+134)724DL_DROPPED = (WM_USER+135)725DL_CANCELDRAG = (WM_USER+136)726DL_CURSORSET = 0727DL_STOPCURSOR = 1728DL_COPYCURSOR = 2729DL_MOVECURSOR = 3730DRAGLISTMSGSTRING = "commctrl_DragListMsg"731UPDOWN_CLASSA = "msctls_updown32"732UPDOWN_CLASS = UPDOWN_CLASSA733UD_MAXVAL = 32767734UD_MINVAL = (-UD_MAXVAL)735UDS_WRAP = 1736UDS_SETBUDDYINT = 2737UDS_ALIGNRIGHT = 4738UDS_ALIGNLEFT = 8739UDS_AUTOBUDDY = 16740UDS_ARROWKEYS = 32741UDS_HORZ = 64742UDS_NOTHOUSANDS = 128743UDS_HOTTRACK = 256744UDM_SETRANGE = (WM_USER+101)745UDM_GETRANGE = (WM_USER+102)746UDM_SETPOS = (WM_USER+103)747UDM_GETPOS = (WM_USER+104)748UDM_SETBUDDY = (WM_USER+105)749UDM_GETBUDDY = (WM_USER+106)750UDM_SETACCEL = (WM_USER+107)751UDM_GETACCEL = (WM_USER+108)752UDM_SETBASE = (WM_USER+109)753UDM_GETBASE = (WM_USER+110)754UDM_SETRANGE32 = (WM_USER+111)755UDM_GETRANGE32 = (WM_USER+112) # wParam & lParam are LPINT756UDM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT757UDM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT758UDN_DELTAPOS = (UDN_FIRST - 1)759PROGRESS_CLASSA = "msctls_progress32"760PROGRESS_CLASS = PROGRESS_CLASSA761PBS_SMOOTH = 1762PBS_VERTICAL = 4763PBM_SETRANGE = (WM_USER+1)764PBM_SETPOS = (WM_USER+2)765PBM_DELTAPOS = (WM_USER+3)766PBM_SETSTEP = (WM_USER+4)767PBM_STEPIT = (WM_USER+5)768PBM_SETRANGE32 = (WM_USER+6) # lParam = high, wParam = low769PBM_GETRANGE = (WM_USER+7) # wParam = return (TRUE ? low : high). lParam = PPBRANGE or NULL770PBM_GETPOS = (WM_USER+8)771PBM_SETBARCOLOR = (WM_USER+9) # lParam = bar color772PBM_SETBKCOLOR = CCM_SETBKCOLOR # lParam = bkColor773HOTKEYF_SHIFT = 1774HOTKEYF_CONTROL = 2775HOTKEYF_ALT = 4776HOTKEYF_EXT = 128777HOTKEYF_EXT = 8778HKCOMB_NONE = 1779HKCOMB_S = 2780HKCOMB_C = 4781HKCOMB_A = 8782HKCOMB_SC = 16783HKCOMB_SA = 32784HKCOMB_CA = 64785HKCOMB_SCA = 128786HKM_SETHOTKEY = (WM_USER+1)787HKM_GETHOTKEY = (WM_USER+2)788HKM_SETRULES = (WM_USER+3)789HOTKEY_CLASSA = "msctls_hotkey32"790HOTKEY_CLASS = HOTKEY_CLASSA791CCS_TOP = 0x00000001792CCS_NOMOVEY = 0x00000002793CCS_BOTTOM = 0x00000003794CCS_NORESIZE = 0x00000004795CCS_NOPARENTALIGN = 0x00000008796CCS_ADJUSTABLE = 0x00000020797CCS_NODIVIDER = 0x00000040798CCS_VERT = 0x00000080799CCS_LEFT = (CCS_VERT | CCS_TOP)800CCS_RIGHT = (CCS_VERT | CCS_BOTTOM)801CCS_NOMOVEX = (CCS_VERT | CCS_NOMOVEY)802WC_LISTVIEWA = "SysListView32"803WC_LISTVIEW = WC_LISTVIEWA804LVS_ICON = 0805LVS_REPORT = 1806LVS_SMALLICON = 2807LVS_LIST = 3808LVS_TYPEMASK = 3809LVS_SINGLESEL = 4810LVS_SHOWSELALWAYS = 8811LVS_SORTASCENDING = 16812LVS_SORTDESCENDING = 32813LVS_SHAREIMAGELISTS = 64814LVS_NOLABELWRAP = 128815LVS_AUTOARRANGE = 256816LVS_EDITLABELS = 512817LVS_OWNERDATA = 4096818LVS_NOSCROLL = 8192819LVS_TYPESTYLEMASK = 64512820LVS_ALIGNTOP = 0821LVS_ALIGNLEFT = 2048822LVS_ALIGNMASK = 3072823LVS_OWNERDRAWFIXED = 1024824LVS_NOCOLUMNHEADER = 16384825LVS_NOSORTHEADER = 32768826LVM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT827LVM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT828LVM_GETBKCOLOR = (LVM_FIRST + 0)829LVM_SETBKCOLOR = (LVM_FIRST + 1)830LVM_GETIMAGELIST = (LVM_FIRST + 2)831LVSIL_NORMAL = 0832LVSIL_SMALL = 1833LVSIL_STATE = 2834LVM_SETIMAGELIST = (LVM_FIRST + 3)835LVM_GETITEMCOUNT = (LVM_FIRST + 4)836LVIF_TEXT = 1837LVIF_IMAGE = 2838LVIF_PARAM = 4839LVIF_STATE = 8840LVIF_INDENT = 16841LVIF_NORECOMPUTE = 2048842LVIS_FOCUSED = 1843LVIS_SELECTED = 2844LVIS_CUT = 4845LVIS_DROPHILITED = 8846LVIS_ACTIVATING = 32847LVIS_OVERLAYMASK = 3840848LVIS_STATEIMAGEMASK = 61440849I_INDENTCALLBACK = (-1)850LPSTR_TEXTCALLBACKA = -1851LPSTR_TEXTCALLBACK = LPSTR_TEXTCALLBACKA852I_IMAGECALLBACK = (-1)853LVM_GETITEMA = (LVM_FIRST + 5)854LVM_GETITEMW = (LVM_FIRST + 75)855LVM_GETITEM = LVM_GETITEMW856LVM_GETITEM = LVM_GETITEMA857LVM_SETITEMA = (LVM_FIRST + 6)858LVM_SETITEMW = (LVM_FIRST + 76)859LVM_SETITEM = LVM_SETITEMW860LVM_SETITEM = LVM_SETITEMA861LVM_INSERTITEMA = (LVM_FIRST + 7)862LVM_INSERTITEMW = (LVM_FIRST + 77)863LVM_INSERTITEM = LVM_INSERTITEMA864LVM_DELETEITEM = (LVM_FIRST + 8)865LVM_DELETEALLITEMS = (LVM_FIRST + 9)866LVM_GETCALLBACKMASK = (LVM_FIRST + 10)867LVM_SETCALLBACKMASK = (LVM_FIRST + 11)868LVNI_ALL = 0869LVNI_FOCUSED = 1870LVNI_SELECTED = 2871LVNI_CUT = 4872LVNI_DROPHILITED = 8873LVNI_ABOVE = 256874LVNI_BELOW = 512875LVNI_TOLEFT = 1024876LVNI_TORIGHT = 2048877LVM_GETNEXTITEM = (LVM_FIRST + 12)878LVFI_PARAM = 1879LVFI_STRING = 2880LVFI_PARTIAL = 8881LVFI_WRAP = 32882LVFI_NEARESTXY = 64883LVM_FINDITEMA = (LVM_FIRST + 13)884LVM_FINDITEMW = (LVM_FIRST + 83)885LVM_FINDITEM = LVM_FINDITEMA886LVIR_BOUNDS = 0887LVIR_ICON = 1888LVIR_LABEL = 2889LVIR_SELECTBOUNDS = 3890LVM_GETITEMRECT = (LVM_FIRST + 14)891LVM_SETITEMPOSITION = (LVM_FIRST + 15)892LVM_GETITEMPOSITION = (LVM_FIRST + 16)893LVM_GETSTRINGWIDTHA = (LVM_FIRST + 17)894LVM_GETSTRINGWIDTHW = (LVM_FIRST + 87)895LVM_GETSTRINGWIDTH = LVM_GETSTRINGWIDTHA896LVHT_NOWHERE = 1897LVHT_ONITEMICON = 2898LVHT_ONITEMLABEL = 4899LVHT_ONITEMSTATEICON = 8900LVHT_ONITEM = (LVHT_ONITEMICON | LVHT_ONITEMLABEL | LVHT_ONITEMSTATEICON)901LVHT_ABOVE = 8902LVHT_BELOW = 16903LVHT_TORIGHT = 32904LVHT_TOLEFT = 64905LVM_HITTEST = (LVM_FIRST + 18)906LVM_ENSUREVISIBLE = (LVM_FIRST + 19)907LVM_SCROLL = (LVM_FIRST + 20)908LVM_REDRAWITEMS = (LVM_FIRST + 21)909LVA_DEFAULT = 0910LVA_ALIGNLEFT = 1911LVA_ALIGNTOP = 2912LVA_SNAPTOGRID = 5913LVM_ARRANGE = (LVM_FIRST + 22)914LVM_EDITLABELA = (LVM_FIRST + 23)915LVM_EDITLABELW = (LVM_FIRST + 118)916LVM_EDITLABEL = LVM_EDITLABELW917LVM_EDITLABEL = LVM_EDITLABELA918LVM_GETEDITCONTROL = (LVM_FIRST + 24)919LVCF_FMT = 1920LVCF_WIDTH = 2921LVCF_TEXT = 4922LVCF_SUBITEM = 8923LVCF_IMAGE = 16924LVCF_ORDER = 32925LVCFMT_LEFT = 0926LVCFMT_RIGHT = 1927LVCFMT_CENTER = 2928LVCFMT_JUSTIFYMASK = 3929LVCFMT_IMAGE = 2048930LVCFMT_BITMAP_ON_RIGHT = 4096931LVCFMT_COL_HAS_IMAGES = 32768932LVM_GETCOLUMNA = (LVM_FIRST + 25)933LVM_GETCOLUMNW = (LVM_FIRST + 95)934LVM_GETCOLUMN = LVM_GETCOLUMNA935LVM_SETCOLUMNA = (LVM_FIRST + 26)936LVM_SETCOLUMNW = (LVM_FIRST + 96)937LVM_SETCOLUMN = LVM_SETCOLUMNA938LVM_INSERTCOLUMNA = (LVM_FIRST + 27)939LVM_INSERTCOLUMNW = (LVM_FIRST + 97)940LVM_INSERTCOLUMN = LVM_INSERTCOLUMNA941LVM_DELETECOLUMN = (LVM_FIRST + 28)942LVM_GETCOLUMNWIDTH = (LVM_FIRST + 29)943LVSCW_AUTOSIZE = -1944LVSCW_AUTOSIZE_USEHEADER = -2945LVM_SETCOLUMNWIDTH = (LVM_FIRST + 30)946LVM_GETHEADER = (LVM_FIRST + 31)947LVM_CREATEDRAGIMAGE = (LVM_FIRST + 33)948LVM_GETVIEWRECT = (LVM_FIRST + 34)949LVM_GETTEXTCOLOR = (LVM_FIRST + 35)950LVM_SETTEXTCOLOR = (LVM_FIRST + 36)951LVM_GETTEXTBKCOLOR = (LVM_FIRST + 37)952LVM_SETTEXTBKCOLOR = (LVM_FIRST + 38)953LVM_GETTOPINDEX = (LVM_FIRST + 39)954LVM_GETCOUNTPERPAGE = (LVM_FIRST + 40)955LVM_GETORIGIN = (LVM_FIRST + 41)956LVM_UPDATE = (LVM_FIRST + 42)957LVM_SETITEMSTATE = (LVM_FIRST + 43)958LVM_GETITEMSTATE = (LVM_FIRST + 44)959LVM_GETITEMTEXTA = (LVM_FIRST + 45)960LVM_GETITEMTEXTW = (LVM_FIRST + 115)961LVM_GETITEMTEXT = LVM_GETITEMTEXTW962LVM_GETITEMTEXT = LVM_GETITEMTEXTA963LVM_SETITEMTEXTA = (LVM_FIRST + 46)964LVM_SETITEMTEXTW = (LVM_FIRST + 116)965LVM_SETITEMTEXT = LVM_SETITEMTEXTW966LVM_SETITEMTEXT = LVM_SETITEMTEXTA967LVSICF_NOINVALIDATEALL = 1968LVSICF_NOSCROLL = 2969LVM_SETITEMCOUNT = (LVM_FIRST + 47)970LVM_SORTITEMS = (LVM_FIRST + 48)971LVM_SETITEMPOSITION32 = (LVM_FIRST + 49)972LVM_GETSELECTEDCOUNT = (LVM_FIRST + 50)973LVM_GETITEMSPACING = (LVM_FIRST + 51)974LVM_GETISEARCHSTRINGA = (LVM_FIRST + 52)975LVM_GETISEARCHSTRINGW = (LVM_FIRST + 117)976LVM_GETISEARCHSTRING = LVM_GETISEARCHSTRINGA977LVM_SETICONSPACING = (LVM_FIRST + 53)978LVM_SETEXTENDEDLISTVIEWSTYLE = (LVM_FIRST + 54) # optional wParam == mask979LVM_GETEXTENDEDLISTVIEWSTYLE = (LVM_FIRST + 55)980LVS_EX_GRIDLINES = 1981LVS_EX_SUBITEMIMAGES = 2982LVS_EX_CHECKBOXES = 4983LVS_EX_TRACKSELECT = 8984LVS_EX_HEADERDRAGDROP = 16985LVS_EX_FULLROWSELECT = 32 # applies to report mode only986LVS_EX_ONECLICKACTIVATE = 64987LVS_EX_TWOCLICKACTIVATE = 128988LVS_EX_FLATSB = 256989LVS_EX_REGIONAL = 512990LVS_EX_INFOTIP = 1024 # listview does InfoTips for you991LVS_EX_UNDERLINEHOT = 2048992LVS_EX_UNDERLINECOLD = 4096993LVS_EX_MULTIWORKAREAS = 8192994LVM_GETSUBITEMRECT = (LVM_FIRST + 56)995LVM_SUBITEMHITTEST = (LVM_FIRST + 57)996LVM_SETCOLUMNORDERARRAY = (LVM_FIRST + 58)997LVM_GETCOLUMNORDERARRAY = (LVM_FIRST + 59)998LVM_SETHOTITEM = (LVM_FIRST + 60)999LVM_GETHOTITEM = (LVM_FIRST + 61)1000LVM_SETHOTCURSOR = (LVM_FIRST + 62)1001LVM_GETHOTCURSOR = (LVM_FIRST + 63)1002LVM_APPROXIMATEVIEWRECT = (LVM_FIRST + 64)1003LV_MAX_WORKAREAS = 161004LVM_SETWORKAREAS = (LVM_FIRST + 65)1005LVM_GETWORKAREAS = (LVM_FIRST + 70)1006LVM_GETNUMBEROFWORKAREAS = (LVM_FIRST + 73)1007LVM_GETSELECTIONMARK = (LVM_FIRST + 66)1008LVM_SETSELECTIONMARK = (LVM_FIRST + 67)1009LVM_SETHOVERTIME = (LVM_FIRST + 71)1010LVM_GETHOVERTIME = (LVM_FIRST + 72)1011LVM_SETTOOLTIPS = (LVM_FIRST + 74)1012LVM_GETTOOLTIPS = (LVM_FIRST + 78)1013LVBKIF_SOURCE_NONE = 01014LVBKIF_SOURCE_HBITMAP = 11015LVBKIF_SOURCE_URL = 21016LVBKIF_SOURCE_MASK = 31017LVBKIF_STYLE_NORMAL = 01018LVBKIF_STYLE_TILE = 161019LVBKIF_STYLE_MASK = 161020LVM_SETBKIMAGEA = (LVM_FIRST + 68)1021LVM_SETBKIMAGEW = (LVM_FIRST + 138)1022LVM_GETBKIMAGEA = (LVM_FIRST + 69)1023LVM_GETBKIMAGEW = (LVM_FIRST + 139)1024LVKF_ALT = 11025LVKF_CONTROL = 21026LVKF_SHIFT = 41027LVN_ITEMCHANGING = (LVN_FIRST-0)1028LVN_ITEMCHANGED = (LVN_FIRST-1)1029LVN_INSERTITEM = (LVN_FIRST-2)1030LVN_DELETEITEM = (LVN_FIRST-3)1031LVN_DELETEALLITEMS = (LVN_FIRST-4)1032LVN_BEGINLABELEDITA = (LVN_FIRST-5)1033LVN_BEGINLABELEDITW = (LVN_FIRST-75)1034LVN_ENDLABELEDITA = (LVN_FIRST-6)1035LVN_ENDLABELEDITW = (LVN_FIRST-76)1036LVN_COLUMNCLICK = (LVN_FIRST-8)1037LVN_BEGINDRAG = (LVN_FIRST-9)1038LVN_BEGINRDRAG = (LVN_FIRST-11)1039LVN_ODCACHEHINT = (LVN_FIRST-13)1040LVN_ODFINDITEMA = (LVN_FIRST-52)1041LVN_ODFINDITEMW = (LVN_FIRST-79)1042LVN_ITEMACTIVATE = (LVN_FIRST-14)1043LVN_ODSTATECHANGED = (LVN_FIRST-15)1044LVN_ODFINDITEM = LVN_ODFINDITEMA1045LVN_HOTTRACK = (LVN_FIRST-21)1046LVN_GETDISPINFOA = (LVN_FIRST-50)1047LVN_GETDISPINFOW = (LVN_FIRST-77)1048LVN_SETDISPINFOA = (LVN_FIRST-51)1049LVN_SETDISPINFOW = (LVN_FIRST-78)1050LVN_BEGINLABELEDIT = LVN_BEGINLABELEDITA1051LVN_ENDLABELEDIT = LVN_ENDLABELEDITA1052LVN_GETDISPINFO = LVN_GETDISPINFOA1053LVN_SETDISPINFO = LVN_SETDISPINFOA1054LVIF_DI_SETITEM = 40961055LVN_KEYDOWN = (LVN_FIRST-55)1056LVN_MARQUEEBEGIN = (LVN_FIRST-56)1057LVGIT_UNFOLDED = 11058LVN_GETINFOTIPA = (LVN_FIRST-57)1059LVN_GETINFOTIPW = (LVN_FIRST-58)1060LVN_GETINFOTIP = LVN_GETINFOTIPA1061WC_TREEVIEWA = "SysTreeView32"1062WC_TREEVIEW = WC_TREEVIEWA1063TVS_HASBUTTONS = 11064TVS_HASLINES = 21065TVS_LINESATROOT = 41066TVS_EDITLABELS = 81067TVS_DISABLEDRAGDROP = 161068TVS_SHOWSELALWAYS = 321069TVS_RTLREADING = 641070TVS_NOTOOLTIPS = 1281071TVS_CHECKBOXES = 2561072TVS_TRACKSELECT = 5121073TVS_SINGLEEXPAND = 10241074TVS_INFOTIP = 20481075TVS_FULLROWSELECT = 40961076TVS_NOSCROLL = 81921077TVS_NONEVENHEIGHT = 163841078TVIF_TEXT = 11079TVIF_IMAGE = 21080TVIF_PARAM = 41081TVIF_STATE = 81082TVIF_HANDLE = 161083TVIF_SELECTEDIMAGE = 321084TVIF_CHILDREN = 641085TVIF_INTEGRAL = 1281086TVIS_SELECTED = 21087TVIS_CUT = 41088TVIS_DROPHILITED = 81089TVIS_BOLD = 161090TVIS_EXPANDED = 321091TVIS_EXPANDEDONCE = 641092TVIS_EXPANDPARTIAL = 1281093TVIS_OVERLAYMASK = 38401094TVIS_STATEIMAGEMASK = 614401095TVIS_USERMASK = 614401096I_CHILDRENCALLBACK = (-1)1097TVI_ROOT = -655361098TVI_FIRST = -655351099TVI_LAST = -655341100TVI_SORT = -655331101TVM_INSERTITEMA = (TV_FIRST + 0)1102TVM_INSERTITEMW = (TV_FIRST + 50)1103TVM_INSERTITEM = TVM_INSERTITEMW1104TVM_INSERTITEM = TVM_INSERTITEMA1105TVM_DELETEITEM = (TV_FIRST + 1)1106TVM_EXPAND = (TV_FIRST + 2)1107TVE_COLLAPSE = 11108TVE_EXPAND = 21109TVE_TOGGLE = 31110TVE_EXPANDPARTIAL = 163841111TVE_COLLAPSERESET = 327681112TVM_GETITEMRECT = (TV_FIRST + 4)1113TVM_GETCOUNT = (TV_FIRST + 5)1114TVM_GETINDENT = (TV_FIRST + 6)1115TVM_SETINDENT = (TV_FIRST + 7)1116TVM_GETIMAGELIST = (TV_FIRST + 8)1117TVSIL_NORMAL = 01118TVSIL_STATE = 21119TVM_SETIMAGELIST = (TV_FIRST + 9)1120TVM_GETNEXTITEM = (TV_FIRST + 10)1121TVGN_ROOT = 01122TVGN_NEXT = 11123TVGN_PREVIOUS = 21124TVGN_PARENT = 31125TVGN_CHILD = 41126TVGN_FIRSTVISIBLE = 51127TVGN_NEXTVISIBLE = 61128TVGN_PREVIOUSVISIBLE = 71129TVGN_DROPHILITE = 81130TVGN_CARET = 91131TVGN_LASTVISIBLE = 101132TVM_SELECTITEM = (TV_FIRST + 11)1133TVM_GETITEMA = (TV_FIRST + 12)1134TVM_GETITEMW = (TV_FIRST + 62)1135TVM_GETITEM = TVM_GETITEMW1136TVM_GETITEM = TVM_GETITEMA1137TVM_SETITEMA = (TV_FIRST + 13)1138TVM_SETITEMW = (TV_FIRST + 63)1139TVM_SETITEM = TVM_SETITEMW1140TVM_SETITEM = TVM_SETITEMA1141TVM_EDITLABELA = (TV_FIRST + 14)1142TVM_EDITLABELW = (TV_FIRST + 65)1143TVM_EDITLABEL = TVM_EDITLABELW1144TVM_EDITLABEL = TVM_EDITLABELA1145TVM_GETEDITCONTROL = (TV_FIRST + 15)1146TVM_GETVISIBLECOUNT = (TV_FIRST + 16)1147TVM_HITTEST = (TV_FIRST + 17)1148TVHT_NOWHERE = 11149TVHT_ONITEMICON = 21150TVHT_ONITEMLABEL = 41151TVHT_ONITEMINDENT = 81152TVHT_ONITEMBUTTON = 161153TVHT_ONITEMRIGHT = 321154TVHT_ONITEMSTATEICON = 641155TVHT_ABOVE = 2561156TVHT_BELOW = 5121157TVHT_TORIGHT = 10241158TVHT_TOLEFT = 20481159TVHT_ONITEM = (TVHT_ONITEMICON | TVHT_ONITEMLABEL | TVHT_ONITEMSTATEICON)1160TVM_CREATEDRAGIMAGE = (TV_FIRST + 18)1161TVM_SORTCHILDREN = (TV_FIRST + 19)1162TVM_ENSUREVISIBLE = (TV_FIRST + 20)1163TVM_SORTCHILDRENCB = (TV_FIRST + 21)1164TVM_ENDEDITLABELNOW = (TV_FIRST + 22)1165TVM_GETISEARCHSTRINGA = (TV_FIRST + 23)1166TVM_GETISEARCHSTRINGW = (TV_FIRST + 64)1167TVM_GETISEARCHSTRING = TVM_GETISEARCHSTRINGA1168TVM_SETTOOLTIPS = (TV_FIRST + 24)1169TVM_GETTOOLTIPS = (TV_FIRST + 25)1170TVM_SETINSERTMARK = (TV_FIRST + 26)1171TVM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT1172TVM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT1173TVM_SETITEMHEIGHT = (TV_FIRST + 27)1174TVM_GETITEMHEIGHT = (TV_FIRST + 28)1175TVM_SETBKCOLOR = (TV_FIRST + 29)1176TVM_SETTEXTCOLOR = (TV_FIRST + 30)1177TVM_GETBKCOLOR = (TV_FIRST + 31)1178TVM_GETTEXTCOLOR = (TV_FIRST + 32)1179TVM_SETSCROLLTIME = (TV_FIRST + 33)1180TVM_GETSCROLLTIME = (TV_FIRST + 34)1181TVM_SETINSERTMARKCOLOR = (TV_FIRST + 37)1182TVM_GETINSERTMARKCOLOR = (TV_FIRST + 38)1183TVN_SELCHANGINGA = (TVN_FIRST-1)1184TVN_SELCHANGINGW = (TVN_FIRST-50)1185TVN_SELCHANGEDA = (TVN_FIRST-2)1186TVN_SELCHANGEDW = (TVN_FIRST-51)1187TVC_UNKNOWN = 01188TVC_BYMOUSE = 11189TVC_BYKEYBOARD = 21190TVN_GETDISPINFOA = (TVN_FIRST-3)1191TVN_GETDISPINFOW = (TVN_FIRST-52)1192TVN_SETDISPINFOA = (TVN_FIRST-4)1193TVN_SETDISPINFOW = (TVN_FIRST-53)1194TVIF_DI_SETITEM = 40961195TVN_ITEMEXPANDINGA = (TVN_FIRST-5)1196TVN_ITEMEXPANDINGW = (TVN_FIRST-54)1197TVN_ITEMEXPANDEDA = (TVN_FIRST-6)1198TVN_ITEMEXPANDEDW = (TVN_FIRST-55)1199TVN_BEGINDRAGA = (TVN_FIRST-7)1200TVN_BEGINDRAGW = (TVN_FIRST-56)1201TVN_BEGINRDRAGA = (TVN_FIRST-8)1202TVN_BEGINRDRAGW = (TVN_FIRST-57)1203TVN_DELETEITEMA = (TVN_FIRST-9)1204TVN_DELETEITEMW = (TVN_FIRST-58)1205TVN_BEGINLABELEDITA = (TVN_FIRST-10)1206TVN_BEGINLABELEDITW = (TVN_FIRST-59)1207TVN_ENDLABELEDITA = (TVN_FIRST-11)1208TVN_ENDLABELEDITW = (TVN_FIRST-60)1209TVN_KEYDOWN = (TVN_FIRST-12)1210TVN_GETINFOTIPA = (TVN_FIRST-13)1211TVN_GETINFOTIPW = (TVN_FIRST-14)1212TVN_SINGLEEXPAND = (TVN_FIRST-15)1213TVN_SELCHANGING = TVN_SELCHANGINGA1214TVN_SELCHANGED = TVN_SELCHANGEDA1215TVN_GETDISPINFO = TVN_GETDISPINFOA1216TVN_SETDISPINFO = TVN_SETDISPINFOA1217TVN_ITEMEXPANDING = TVN_ITEMEXPANDINGA1218TVN_ITEMEXPANDED = TVN_ITEMEXPANDEDA1219TVN_BEGINDRAG = TVN_BEGINDRAGA1220TVN_BEGINRDRAG = TVN_BEGINRDRAGA1221TVN_DELETEITEM = TVN_DELETEITEMA1222TVN_BEGINLABELEDIT = TVN_BEGINLABELEDITA1223TVN_ENDLABELEDIT = TVN_ENDLABELEDITA1224TVN_GETINFOTIP = TVN_GETINFOTIPA1225TVCDRF_NOIMAGES = 655361226WC_COMBOBOXEXA = "ComboBoxEx32"1227WC_COMBOBOXEX = WC_COMBOBOXEXA1228CBEIF_TEXT = 11229CBEIF_IMAGE = 21230CBEIF_SELECTEDIMAGE = 41231CBEIF_OVERLAY = 81232CBEIF_INDENT = 161233CBEIF_LPARAM = 321234CBEIF_DI_SETITEM = 2684354561235CBEM_INSERTITEMA = (WM_USER + 1)1236CBEM_SETIMAGELIST = (WM_USER + 2)1237CBEM_GETIMAGELIST = (WM_USER + 3)1238CBEM_GETITEMA = (WM_USER + 4)1239CBEM_SETITEMA = (WM_USER + 5)1240#CBEM_DELETEITEM = CB_DELETESTRING1241CBEM_GETCOMBOCONTROL = (WM_USER + 6)1242CBEM_GETEDITCONTROL = (WM_USER + 7)1243CBEM_SETEXSTYLE = (WM_USER + 8) # use SETEXTENDEDSTYLE instead1244CBEM_SETEXTENDEDSTYLE = (WM_USER + 14) # lparam == new style, wParam (optional) == mask1245CBEM_GETEXSTYLE = (WM_USER + 9) # use GETEXTENDEDSTYLE instead1246CBEM_GETEXTENDEDSTYLE = (WM_USER + 9)1247CBEM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT1248CBEM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT1249CBEM_SETEXSTYLE = (WM_USER + 8)1250CBEM_GETEXSTYLE = (WM_USER + 9)1251CBEM_HASEDITCHANGED = (WM_USER + 10)1252CBEM_INSERTITEMW = (WM_USER + 11)1253CBEM_SETITEMW = (WM_USER + 12)1254CBEM_GETITEMW = (WM_USER + 13)1255CBEM_INSERTITEM = CBEM_INSERTITEMA1256CBEM_SETITEM = CBEM_SETITEMA1257CBEM_GETITEM = CBEM_GETITEMA1258CBES_EX_NOEDITIMAGE = 11259CBES_EX_NOEDITIMAGEINDENT = 21260CBES_EX_PATHWORDBREAKPROC = 41261CBES_EX_NOSIZELIMIT = 81262CBES_EX_CASESENSITIVE = 161263CBEN_GETDISPINFO = (CBEN_FIRST - 0)1264CBEN_GETDISPINFOA = (CBEN_FIRST - 0)1265CBEN_INSERTITEM = (CBEN_FIRST - 1)1266CBEN_DELETEITEM = (CBEN_FIRST - 2)1267CBEN_BEGINEDIT = (CBEN_FIRST - 4)1268CBEN_ENDEDITA = (CBEN_FIRST - 5)1269CBEN_ENDEDITW = (CBEN_FIRST - 6)1270CBEN_GETDISPINFOW = (CBEN_FIRST - 7)1271CBEN_DRAGBEGINA = (CBEN_FIRST - 8)1272CBEN_DRAGBEGINW = (CBEN_FIRST - 9)1273CBEN_DRAGBEGIN = CBEN_DRAGBEGINA1274CBEN_ENDEDIT = CBEN_ENDEDITA1275CBENF_KILLFOCUS = 11276CBENF_RETURN = 21277CBENF_ESCAPE = 31278CBENF_DROPDOWN = 41279CBEMAXSTRLEN = 2601280WC_TABCONTROLA = "SysTabControl32"1281WC_TABCONTROL = WC_TABCONTROLA1282TCS_SCROLLOPPOSITE = 1 # assumes multiline tab1283TCS_BOTTOM = 21284TCS_RIGHT = 21285TCS_MULTISELECT = 4 # allow multi-select in button mode1286TCS_FLATBUTTONS = 81287TCS_FORCEICONLEFT = 161288TCS_FORCELABELLEFT = 321289TCS_HOTTRACK = 641290TCS_VERTICAL = 1281291TCS_TABS = 01292TCS_BUTTONS = 2561293TCS_SINGLELINE = 01294TCS_MULTILINE = 5121295TCS_RIGHTJUSTIFY = 01296TCS_FIXEDWIDTH = 10241297TCS_RAGGEDRIGHT = 20481298TCS_FOCUSONBUTTONDOWN = 40961299TCS_OWNERDRAWFIXED = 81921300TCS_TOOLTIPS = 163841301TCS_FOCUSNEVER = 327681302TCS_EX_FLATSEPARATORS = 11303TCS_EX_REGISTERDROP = 21304TCM_GETIMAGELIST = (TCM_FIRST + 2)1305TCM_SETIMAGELIST = (TCM_FIRST + 3)1306TCM_GETITEMCOUNT = (TCM_FIRST + 4)1307TCIF_TEXT = 11308TCIF_IMAGE = 21309TCIF_RTLREADING = 41310TCIF_PARAM = 81311TCIF_STATE = 161312TCIS_BUTTONPRESSED = 11313TCIS_HIGHLIGHTED = 21314TCM_GETITEMA = (TCM_FIRST + 5)1315TCM_GETITEMW = (TCM_FIRST + 60)1316TCM_GETITEM = TCM_GETITEMA1317TCM_SETITEMA = (TCM_FIRST + 6)1318TCM_SETITEMW = (TCM_FIRST + 61)1319TCM_SETITEM = TCM_SETITEMA1320TCM_INSERTITEMA = (TCM_FIRST + 7)1321TCM_INSERTITEMW = (TCM_FIRST + 62)1322TCM_INSERTITEM = TCM_INSERTITEMA1323TCM_DELETEITEM = (TCM_FIRST + 8)1324TCM_DELETEALLITEMS = (TCM_FIRST + 9)1325TCM_GETITEMRECT = (TCM_FIRST + 10)1326TCM_GETCURSEL = (TCM_FIRST + 11)1327TCM_SETCURSEL = (TCM_FIRST + 12)1328TCHT_NOWHERE = 11329TCHT_ONITEMICON = 21330TCHT_ONITEMLABEL = 41331TCHT_ONITEM = (TCHT_ONITEMICON | TCHT_ONITEMLABEL)1332TCM_HITTEST = (TCM_FIRST + 13)1333TCM_SETITEMEXTRA = (TCM_FIRST + 14)1334TCM_ADJUSTRECT = (TCM_FIRST + 40)1335TCM_SETITEMSIZE = (TCM_FIRST + 41)1336TCM_REMOVEIMAGE = (TCM_FIRST + 42)1337TCM_SETPADDING = (TCM_FIRST + 43)1338TCM_GETROWCOUNT = (TCM_FIRST + 44)1339TCM_GETTOOLTIPS = (TCM_FIRST + 45)1340TCM_SETTOOLTIPS = (TCM_FIRST + 46)1341TCM_GETCURFOCUS = (TCM_FIRST + 47)1342TCM_SETCURFOCUS = (TCM_FIRST + 48)1343TCM_SETMINTABWIDTH = (TCM_FIRST + 49)1344TCM_DESELECTALL = (TCM_FIRST + 50)1345TCM_HIGHLIGHTITEM = (TCM_FIRST + 51)1346TCM_SETEXTENDEDSTYLE = (TCM_FIRST + 52) # optional wParam == mask1347TCM_GETEXTENDEDSTYLE = (TCM_FIRST + 53)1348TCM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT1349TCM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT1350TCN_KEYDOWN = (TCN_FIRST - 0)1351ANIMATE_CLASSA = "SysAnimate32"1352ANIMATE_CLASS = ANIMATE_CLASSA1353ACS_CENTER = 11354ACS_TRANSPARENT = 21355ACS_AUTOPLAY = 41356ACS_TIMER = 8 # don't use threads... use timers1357ACM_OPENA = (WM_USER+100)1358ACM_OPENW = (WM_USER+103)1359ACM_OPEN = ACM_OPENW1360ACM_OPEN = ACM_OPENA1361ACM_PLAY = (WM_USER+101)1362ACM_STOP = (WM_USER+102)1363ACN_START = 11364ACN_STOP = 21365MONTHCAL_CLASSA = "SysMonthCal32"1366MONTHCAL_CLASS = MONTHCAL_CLASSA1367MCM_FIRST = 40961368MCM_GETCURSEL = (MCM_FIRST + 1)1369MCM_SETCURSEL = (MCM_FIRST + 2)1370MCM_GETMAXSELCOUNT = (MCM_FIRST + 3)1371MCM_SETMAXSELCOUNT = (MCM_FIRST + 4)1372MCM_GETSELRANGE = (MCM_FIRST + 5)1373MCM_SETSELRANGE = (MCM_FIRST + 6)1374MCM_GETMONTHRANGE = (MCM_FIRST + 7)1375MCM_SETDAYSTATE = (MCM_FIRST + 8)1376MCM_GETMINREQRECT = (MCM_FIRST + 9)1377MCM_SETCOLOR = (MCM_FIRST + 10)1378MCM_GETCOLOR = (MCM_FIRST + 11)1379MCSC_BACKGROUND = 0 # the background color (between months)1380MCSC_TEXT = 1 # the dates1381MCSC_TITLEBK = 2 # background of the title1382MCSC_TITLETEXT = 31383MCSC_MONTHBK = 4 # background within the month cal1384MCSC_TRAILINGTEXT = 5 # the text color of header & trailing days1385MCM_SETTODAY = (MCM_FIRST + 12)1386MCM_GETTODAY = (MCM_FIRST + 13)1387MCM_HITTEST = (MCM_FIRST + 14)1388MCHT_TITLE = 655361389MCHT_CALENDAR = 1310721390MCHT_TODAYLINK = 1966081391MCHT_NEXT = 16777216 # these indicate that hitting1392MCHT_PREV = 33554432 # here will go to the next/prev month1393MCHT_NOWHERE = 01394MCHT_TITLEBK = (MCHT_TITLE)1395MCHT_TITLEMONTH = (MCHT_TITLE | 1)1396MCHT_TITLEYEAR = (MCHT_TITLE | 2)1397MCHT_TITLEBTNNEXT = (MCHT_TITLE | MCHT_NEXT | 3)1398MCHT_TITLEBTNPREV = (MCHT_TITLE | MCHT_PREV | 3)1399MCHT_CALENDARBK = (MCHT_CALENDAR)1400MCHT_CALENDARDATE = (MCHT_CALENDAR | 1)1401MCHT_CALENDARDATENEXT = (MCHT_CALENDARDATE | MCHT_NEXT)1402MCHT_CALENDARDATEPREV = (MCHT_CALENDARDATE | MCHT_PREV)1403MCHT_CALENDARDAY = (MCHT_CALENDAR | 2)1404MCHT_CALENDARWEEKNUM = (MCHT_CALENDAR | 3)1405MCM_SETFIRSTDAYOFWEEK = (MCM_FIRST + 15)1406MCM_GETFIRSTDAYOFWEEK = (MCM_FIRST + 16)1407MCM_GETRANGE = (MCM_FIRST + 17)1408MCM_SETRANGE = (MCM_FIRST + 18)1409MCM_GETMONTHDELTA = (MCM_FIRST + 19)1410MCM_SETMONTHDELTA = (MCM_FIRST + 20)1411MCM_GETMAXTODAYWIDTH = (MCM_FIRST + 21)1412MCM_SETUNICODEFORMAT = CCM_SETUNICODEFORMAT1413MCM_GETUNICODEFORMAT = CCM_GETUNICODEFORMAT1414MCN_SELCHANGE = (MCN_FIRST + 1)1415MCN_GETDAYSTATE = (MCN_FIRST + 3)1416MCN_SELECT = (MCN_FIRST + 4)1417MCS_DAYSTATE = 11418MCS_MULTISELECT = 21419MCS_WEEKNUMBERS = 41420MCS_NOTODAYCIRCLE = 81421MCS_NOTODAY = 161422MCS_NOTODAY = 81423GMR_VISIBLE = 0 # visible portion of display1424GMR_DAYSTATE = 1 # above plus the grayed out parts of1425DATETIMEPICK_CLASSA = "SysDateTimePick32"1426DATETIMEPICK_CLASS = DATETIMEPICK_CLASSA1427DTM_FIRST = 40961428DTM_GETSYSTEMTIME = (DTM_FIRST + 1)1429DTM_SETSYSTEMTIME = (DTM_FIRST + 2)1430DTM_GETRANGE = (DTM_FIRST + 3)1431DTM_SETRANGE = (DTM_FIRST + 4)1432DTM_SETFORMATA = (DTM_FIRST + 5)1433DTM_SETFORMATW = (DTM_FIRST + 50)1434DTM_SETFORMAT = DTM_SETFORMATW1435DTM_SETFORMAT = DTM_SETFORMATA1436DTM_SETMCCOLOR = (DTM_FIRST + 6)1437DTM_GETMCCOLOR = (DTM_FIRST + 7)1438DTM_GETMONTHCAL = (DTM_FIRST + 8)1439DTM_SETMCFONT = (DTM_FIRST + 9)1440DTM_GETMCFONT = (DTM_FIRST + 10)1441DTS_UPDOWN = 1 # use UPDOWN instead of MONTHCAL1442DTS_SHOWNONE = 2 # allow a NONE selection1443DTS_SHORTDATEFORMAT = 0 # use the short date format (app must forward WM_WININICHANGE messages)1444DTS_LONGDATEFORMAT = 4 # use the long date format (app must forward WM_WININICHANGE messages)1445DTS_TIMEFORMAT = 9 # use the time format (app must forward WM_WININICHANGE messages)1446DTS_APPCANPARSE = 16 # allow user entered strings (app MUST respond to DTN_USERSTRING)1447DTS_RIGHTALIGN = 32 # right-align popup instead of left-align it1448DTN_DATETIMECHANGE = (DTN_FIRST + 1) # the systemtime has changed1449DTN_USERSTRINGA = (DTN_FIRST + 2) # the user has entered a string1450DTN_USERSTRINGW = (DTN_FIRST + 15)1451DTN_USERSTRING = DTN_USERSTRINGW1452DTN_WMKEYDOWNA = (DTN_FIRST + 3) # modify keydown on app format field (X)1453DTN_WMKEYDOWNW = (DTN_FIRST + 16)1454DTN_WMKEYDOWN = DTN_WMKEYDOWNA1455DTN_FORMATA = (DTN_FIRST + 4) # query display for app format field (X)1456DTN_FORMATW = (DTN_FIRST + 17)1457DTN_FORMAT = DTN_FORMATA1458DTN_FORMATQUERYA = (DTN_FIRST + 5) # query formatting info for app format field (X)1459DTN_FORMATQUERYW = (DTN_FIRST + 18)1460DTN_FORMATQUERY = DTN_FORMATQUERYA1461DTN_DROPDOWN = (DTN_FIRST + 6) # MonthCal has dropped down1462DTN_CLOSEUP = (DTN_FIRST + 7) # MonthCal is popping up1463GDTR_MIN = 11464GDTR_MAX = 21465GDT_ERROR = -11466GDT_VALID = 01467GDT_NONE = 11468IPM_CLEARADDRESS = (WM_USER+100) # no parameters1469IPM_SETADDRESS = (WM_USER+101) # lparam = TCP/IP address1470IPM_GETADDRESS = (WM_USER+102) # lresult = # of non black fields. lparam = LPDWORD for TCP/IP address1471IPM_SETRANGE = (WM_USER+103) # wparam = field, lparam = range1472IPM_SETFOCUS = (WM_USER+104) # wparam = field1473IPM_ISBLANK = (WM_USER+105) # no parameters1474WC_IPADDRESSA = "SysIPAddress32"1475WC_IPADDRESS = WC_IPADDRESSA1476IPN_FIELDCHANGED = (IPN_FIRST - 0)1477WC_PAGESCROLLERA = "SysPager"1478WC_PAGESCROLLER = WC_PAGESCROLLERA1479PGS_VERT = 01480PGS_HORZ = 11481PGS_AUTOSCROLL = 21482PGS_DRAGNDROP = 41483PGF_INVISIBLE = 0 # Scroll button is not visible1484PGF_NORMAL = 1 # Scroll button is in normal state1485PGF_GRAYED = 2 # Scroll button is in grayed state1486PGF_DEPRESSED = 4 # Scroll button is in depressed state1487PGF_HOT = 8 # Scroll button is in hot state1488PGB_TOPORLEFT = 01489PGB_BOTTOMORRIGHT = 11490PGM_SETCHILD = (PGM_FIRST + 1) # lParam == hwnd1491PGM_RECALCSIZE = (PGM_FIRST + 2)1492PGM_FORWARDMOUSE = (PGM_FIRST + 3)1493PGM_SETBKCOLOR = (PGM_FIRST + 4)1494PGM_GETBKCOLOR = (PGM_FIRST + 5)1495PGM_SETBORDER = (PGM_FIRST + 6)1496PGM_GETBORDER = (PGM_FIRST + 7)1497PGM_SETPOS = (PGM_FIRST + 8)1498PGM_GETPOS = (PGM_FIRST + 9)1499PGM_SETBUTTONSIZE = (PGM_FIRST + 10)1500PGM_GETBUTTONSIZE = (PGM_FIRST + 11)1501PGM_GETBUTTONSTATE = (PGM_FIRST + 12)1502PGM_GETDROPTARGET = CCM_GETDROPTARGET1503PGN_SCROLL = (PGN_FIRST-1)1504PGF_SCROLLUP = 11505PGF_SCROLLDOWN = 21506PGF_SCROLLLEFT = 41507PGF_SCROLLRIGHT = 81508PGK_SHIFT = 11509PGK_CONTROL = 21510PGK_MENU = 41511PGN_CALCSIZE = (PGN_FIRST-2)1512PGF_CALCWIDTH = 11513PGF_CALCHEIGHT = 21514WC_NATIVEFONTCTLA = "NativeFontCtl"1515WC_NATIVEFONTCTL = WC_NATIVEFONTCTLA1516NFS_EDIT = 11517NFS_STATIC = 21518NFS_LISTCOMBO = 41519NFS_BUTTON = 81520NFS_ALL = 161521WM_MOUSEHOVER = 6731522WM_MOUSELEAVE = 6751523TME_HOVER = 11524TME_LEAVE = 21525TME_QUERY = 10737418241526TME_CANCEL = -21474836481527HOVER_DEFAULT = -11528WSB_PROP_CYVSCROLL = 0x000000011529WSB_PROP_CXHSCROLL = 0x000000021530WSB_PROP_CYHSCROLL = 0x000000041531WSB_PROP_CXVSCROLL = 0x000000081532WSB_PROP_CXHTHUMB = 0x000000101533WSB_PROP_CYVTHUMB = 0x000000201534WSB_PROP_VBKGCOLOR = 0x000000401535WSB_PROP_HBKGCOLOR = 0x000000801536WSB_PROP_VSTYLE = 0x000001001537WSB_PROP_HSTYLE = 0x000002001538WSB_PROP_WINSTYLE = 0x000004001539WSB_PROP_PALETTE = 0x000008001540WSB_PROP_MASK = 0x00000FFF1541FSB_FLAT_MODE = 21542FSB_ENCARTA_MODE = 11543FSB_REGULAR_MODE = 01544def INDEXTOOVERLAYMASK(i):1545 return i << 81546def INDEXTOSTATEIMAGEMASK(i):...

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handle.py

Source:handle.py Github

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...238 yield scrapy.Request("http://www.realax.cn/browse?type=dateissued", callback=self.parse_first)239 yield scrapy.Request("http://ir.sia.cn/browse?type=dateissued", callback=self.parse_first)240 yield scrapy.Request("http://ir.stlib.cn/browse?type=dateissued", callback=self.parse_first)241 # yield scrapy.Request("")242 def parse_first(self, response):243 self.log("Crawled %s %d" % (response.url, response.status), level=scrapy.log.INFO)244 if response.status / 100 != 2:245 return246 # 从网页中提取最大文档数量，用于构造索引页翻页247 ret = re.search("var totalItemCount = (\d+);", response.body)248 totalItemCount = 0249 if ret:250 totalItemCount = int(ret.groups()[0])251 self.log("Parse %s totalItemCount %d" % (response.url, totalItemCount), level=scrapy.log.INFO)252 else:253 self.log("Parse %s totalItemCount NULL" % (response.url), level=scrapy.log.INFO)254 offset = 0255 # site = get_url_site(response.url)256 # 构造翻页并抓取...

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ProjectIVs.py

Source:ProjectIVs.py Github

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1import numpy as np2from sklearn.linear_model import LogisticRegression3import statsmodels.formula.api as smf4import matplotlib5matplotlib.use("TkAgg")6from matplotlib import pyplot as plt7from scipy.special import expit8#matplotlib inline9from sklearn.linear_model import LassoCV10from sklearn.linear_model import Ridge11from sklearn.linear_model import LinearRegression12from sklearn.metrics.pairwise import manhattan_distances13import statsmodels.api as sm14from scipy import stats15from linearmodels.iv import IV2SLS16import os, glob17import pandas as pd18#merge sessions19path = "/Users/antoniomoshe/OneDrive - Technion/TECHNION PHD/Winter 2020/Casuality/Devouir_4/Sessions"20all_files = glob.glob(os.path.join(path, "sessions*.csv"))21sessions_month= pd.concat([pd.read_csv(f) for f in all_files])22#---23len(sessions_month) #502,03324#calculate average service time per hour25sessions_month.columns26sessions_AcceptedInvo = sessions_month[sessions_month[" outcome"] < 5]27(sessions_AcceptedInvo[" queue_sec"] == 0).sum() #all are known abandonments28sessions_AcceptedInvo = sessions_AcceptedInvo[sessions_AcceptedInvo[" queue_sec"] > 0]29len(sessions_AcceptedInvo) #2338330#get StartDay and StartHour for invitation acceptance time ------31#convert strings into date32sessions_AcceptedInvo[' invitation_submit_date'] =\33 pd.to_datetime(sessions_AcceptedInvo[' invitation_submit_date'],\34 format=' %d/%m/%Y %H:%M:%S')35sessions_AcceptedInvo[' Invitation_Acep_Day_of_week']=\36 sessions_AcceptedInvo[' invitation_submit_date'].dt.dayofweek37sessions_AcceptedInvo['Invitation_Acep_Hour']=\38 sessions_AcceptedInvo[' invitation_submit_date'].dt.hour39#Treatment Wait variable ---Ö±40#short wait is the treatment i would expect them to buy41sessions_AcceptedInvo['WaitTreatment'] = np.where(sessions_AcceptedInvo[' queue_sec']< 60, 1, 0)42#----43sessions_AcceptedInvo['Y'] =\44 np.where(sessions_AcceptedInvo[' conversion_time']> 0, 1, 0)45#calculate total service duration46timezero=sessions_AcceptedInvo[" invitation_submit_time"].min()47sessions_AcceptedInvo['dummy'] =\48sessions_AcceptedInvo[" end_time"]- \49 sessions_AcceptedInvo[" chat_start_time"]50#known abandonments have no service time51sessions_AcceptedInvo['Total_Service_Duration'] =\52 np.where(sessions_AcceptedInvo['dummy']>= timezero, 0, sessions_AcceptedInvo['dummy'])53sessions_AcceptedInvo.drop('dummy', inplace=True, axis=1)54#write csv55sessions_AcceptedInvo.to_csv("sessions_AcceptedInvo.csv")56#using closure time57timemax=sessions_AcceptedInvo[" invitation_submit_time"].max()58#time in seconds59numberofbins=round((timemax-timezero)/3600)60sessions_AcceptedInvo['dummy'] =\61sessions_AcceptedInvo[" end_time"]- \62 sessions_AcceptedInvo[" chat_start_time"]63#known abandonments have no service time64sessions_AcceptedInvo['Total_Service_Duration'] =\65 np.where(sessions_AcceptedInvo['dummy']>= timezero, 0, sessions_AcceptedInvo['dummy'])66sessions_AcceptedInvo.drop('dummy', inplace=True, axis=1)67sessions_AcceptedInvo['Total_Service_Duration'].mean()68( (sessions_AcceptedInvo[' outcome']==1).sum()+(sessions_AcceptedInvo[' outcome']==2).sum()) \69 / (sessions_AcceptedInvo[' queue_sec'].sum())70#check with linear regression what influences outcome Y -----71#and also is data for other treatments72dataForRegression = pd.read_csv('DataForRegression.csv', index_col=0)73regresion_check = IV2SLS(dataForRegression.Y,\74 dataForRegression[['queue_sec','invite_type', 'engagement_skill','target_skill','region','city','country','continent','user_os',\75 'browser','score','other_time','other_lines','other_number_words',\76 'inner_wait', 'visitor_duration',\77 'agent_duration', 'visitor_number_words', 'agent_number_words',\78 'visitor_lines', 'agent_lines', \79 'total_canned_lines', 'average_sent', 'min_sent', 'max_sent', 'n_sent_pos', 'n_sent_neg', 'first_sent',\80 'last_sent', 'id_rep_code', \81 'Invitation_Acep_Day_of_week', 'Invitation_Acep_Hour', \82 'NumberofAssigned', 'NumberofAssignedwhenAssigned', \83 'Rho_atarrival',\84 ]], None, None).fit(cov_type='unadjusted')85print(regresion_check)86regresion_check2 = IV2SLS(dataForRegression.Y,\87 dataForRegression[['queue_sec','invite_type', 'engagement_skill','target_skill','score','other_time',\88 'agent_number_words',\89 'visitor_lines', 'agent_lines', \90 ]], None, None).fit(cov_type='unadjusted')91print(regresion_check2)92#-----93res_first = IV2SLS(dataFirstT.WaitTreatment,dataFirstT.iloc[:, 1:14], None, None).fit(cov_type='unadjusted')94res_first = IV2SLS(dataFirstT.WaitTreatment,dataFirstT.iloc[:, 1:14], None, None).fit(cov_type='unadjusted')95print(res_first)96res_second = IV2SLS(dataFirstT.Y,dataFirstT[['region','city','country','continent','user_os',\97 'browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour'\98 ]],\99 dataFirstT.queue_sec*60, dataFirstT[['Rho_atarrival','invite_type',\100 'engagement_skill','target_skill'\101 ]]).fit(cov_type='unadjusted')102def covariance(x, y):103 # Finding the mean of the series x and y104 mean_x = sum(x)/float(len(x))105 mean_y = sum(y)/float(len(y))106 # Subtracting mean from the individual elements107 sub_x = [i - mean_x for i in x]108 sub_y = [i - mean_y for i in y]109 numerator = sum([sub_x[i]*sub_y[i] for i in range(len(sub_x))])110 denominator = len(x)-1111 cov = numerator/denominator112 return cov113covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)114#-0.12115corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\116 'engagement_skill','target_skill']].corr()117#FIRST TREATMENT118#data that we are using after fix in Matlab119dataFirstTi = pd.read_csv('DataForFirstTreatment.csv', index_col=0)120len(dataFirstTi) #23383121#get only service customers122dataFirstT=dataFirstTi123#len(dataFirstT) #13345124dataFirstT.WaitTreatment\125 = np.where(dataFirstT['queue_sec']< 30, 1, 0)126dataFirstT.WaitTreatment\127 = np.where(dataFirstT['queue_sec']< 60, 1, 0)128#dataFirstT['RhoBinary']\129# = np.where(dataFirstT['Rho_atarrival']< 1, 0, 1)130dFT=dataFirstT[[\131'invite_type','region','city','engagement_skill','target_skill','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\132]]133modelP = LogisticRegression(max_iter=10000)134FirstX = dataFirstT[[\135 'Invitation_Acep_Hour', \136 'Rho_atarrival', 'invite_type', \137 'engagement_skill','target_skill',\138 'Invitation_Acep_Day_of_week',\139 'score','region','city','country','continent',\140 'user_os','browser'\141 ]]142#Wait time143FirstY = dataFirstT.WaitTreatment144modelP.fit(FirstX, FirstY)145#reg2 = LinearRegression().fit(FirstX, FirstY)146First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))147treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')148cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')149plt.legend();150plt.xlabel('propensity score');151plt.ylabel('number of units');152plt.savefig('prop_score_wait_60000.pdf',format='pdf')153#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])154FirstIndex=\155np.where((First_Propensityscore[:, 1] < 0.59)& (First_Propensityscore[:, 1] > 0.41))156First_PropensityscoreB=\157First_Propensityscore[FirstIndex[0]]158treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')159cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')160plt.legend();161plt.xlabel('propensity score');162plt.ylabel('number of units');163plt.savefig('prop_score_wait_6000000_Last.pdf',format='pdf')164dataFirstT2=dFT.iloc[FirstIndex[0]]165len(dataFirstT2) #30 sec 10041 #60 sec 6,316166len(dataFirstT) #23383167ATE_IPW=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\168-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))169dataFirstT.groupby("Y")["queue_sec"].mean()170#Out[132]:171#Y172#0 70.728748173#1 48.358257174# ----- T-learner --------175#dataFirstTB = dataFirstT2[[\176# 'Invitation_Acep_Hour', \177# 'Rho_atarrival', 'invite_type', \178# 'engagement_skill', 'target_skill',\179# 'WaitTreatment', 'Y']]180dataFirstTB=dataFirstT2181dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]182dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)183dataFirstTB0x = dataFirstTB0.iloc[:, :-1]184dataFirstTB0Y = dataFirstTB0['Y']185dataFirstTBx = dataFirstTB.iloc[:, :-1]186dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)187dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]188dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)189dataFirstTB1x = dataFirstTB1.iloc[:, :-1]190dataFirstTB1Y = dataFirstTB1['Y']191model_0 = LassoCV()192model_1 = LassoCV()193model_0.fit(dataFirstTB0x, dataFirstTB0Y)194model_1.fit(dataFirstTB1x, dataFirstTB1Y)195prediction0 = model_0.predict(dataFirstTBx)196prediction1 = model_1.predict(dataFirstTBx)197ATE_TLearner = float((prediction1-prediction0).sum()/len(dataFirstTBx))198# ----- T-learner --------199# ------- S-Learner ---------200dataFirstTBX = dataFirstTB.iloc[:, :-1]201dataFirstTBY = dataFirstTB['Y']202modelS = Ridge()203modelS.fit(dataFirstTBX, dataFirstTBY)204#T=1205dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]206dummT1x = dataFirstTB.iloc[:, :-1]207predictionUn = modelS.predict(dummT1x)208dummT1["WaitTreatment"] = 0209dummT1x = dummT1.iloc[:, :-1]210predictionZe = modelS.predict(dummT1x)211ATE_SLearner = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))212# ------- S-Learner ---------213# ---------matching---------214#T=0215dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]216dummT0 = dummT0.drop(["WaitTreatment"], axis=1)217dummT0x = dummT0.iloc[:, :-1]218dummT0Y = dummT0['Y']219#T=1220dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]221dummT1 = dummT1.drop(["WaitTreatment"], axis=1)222dummT1x = dummT1.iloc[:, :-1]223dummT1Y = dummT1['Y']224dummT0V = dummT0.values225dummT1V = dummT1.values226dummT1XV=dummT1x.values227dummT0XV=dummT0x.values228ITE = []229for row in dummT1V:230 dif=abs(row[:-1]-dummT0V[:,:-1])231 #index232 #dif.argmin()233 #value234 distance=row[-1]-dummT0V[dif.argmin(),-1]235 ITE.append(distance)236#Sum of the Treated/number of Treated237ATE_Matchingb = float(sum(ITE))/float(len(dataFirstTB))238# ---------matching---------239#-----------IV's-------------240corralation=dataFirstTB.corr()241res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['invite_type','engagement_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Hour','Invitation_Acep_Day_of_week']],\242 dataFirstTB.WaitTreatment, dataFirstTB.Rho_atarrival).fit(cov_type='unadjusted')243print(res_second)244covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)245#-0.12246corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\247 'engagement_skill','target_skill']].corr()248#-----------IV's-------------249# treatment two inner Wait ------250dataForRegression['IW_Treatment'] = np.where(dataForRegression['other_time']< 30, 1, 0)251#120 #130252modelP2 = LogisticRegression(max_iter=10000)253SecondX = dataForRegression[[\254 'invite_type', 'engagement_skill','target_skill','region','city','country','continent','user_os',\255 'browser','score','other_number_words',\256 'visitor_duration',\257 'agent_duration', 'visitor_number_words', 'agent_number_words',\258 'total_canned_lines', 'average_sent', 'min_sent', 'max_sent', 'n_sent_pos', 'n_sent_neg', 'first_sent',\259 'last_sent', 'id_rep_code', \260 'Invitation_Acep_Day_of_week', 'Invitation_Acep_Hour', \261 'NumberofAssigned', 'NumberofAssignedwhenAssigned', \262 ]]263SecondY = dataForRegression.IW_Treatment264modelP2.fit(SecondX, SecondY)265Second_Propensityscore = np.asarray(modelP2.predict_proba(SecondX))266treatment_plt2=plt.hist(Second_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')267cont_plt2=plt.hist(Second_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')268plt.legend();269plt.xlabel('propensity score');270plt.ylabel('number of units');271plt.savefig('prop_score_IW2_b.pdf',format='pdf')272ATE_IPW2=(((dataForRegression['IW_Treatment']*dataForRegression['Y'])/(Second_Propensityscore[:, 1])).sum())*(1/len(dataForRegression))\273-( ( ( (1-dataForRegression['IW_Treatment'])*dataForRegression['Y'] )/(Second_Propensityscore[:, 0]) ).sum())*(1/len(dataForRegression))274dataForRegression.groupby("Y")["other_time"].mean()275#Out[132]:276#Y277#0 139.905420278#1 237.277638279dataFirstTB = dataForRegression[[\280 'invite_type', 'engagement_skill','target_skill','region','city','country','continent','user_os',\281 'browser','score','other_number_words',\282 'visitor_duration',\283 'agent_duration', 'visitor_number_words', 'agent_number_words',\284 'total_canned_lines', 'average_sent', 'min_sent', 'max_sent', 'n_sent_pos', 'n_sent_neg', 'first_sent',\285 'last_sent', 'id_rep_code', \286 'Invitation_Acep_Day_of_week', 'Invitation_Acep_Hour', \287 'NumberofAssigned', 'NumberofAssignedwhenAssigned','IW_Treatment','Y',\288 ]]289dataFirstTB0 = dataFirstTB[dataFirstTB["IW_Treatment"] == 0]290dataFirstTB0 = dataFirstTB0.drop(["IW_Treatment"], axis=1)291dataFirstTB0x = dataFirstTB0.iloc[:, :-1]292dataFirstTB0Y = dataFirstTB0['Y']293dataFirstTBx = dataFirstTB.iloc[:, :-1]294dataFirstTBx = dataFirstTBx.drop(["IW_Treatment"], axis=1)295dataFirstTB1 = dataFirstTB[dataFirstTB["IW_Treatment"] == 1]296dataFirstTB1 = dataFirstTB1.drop(["IW_Treatment"], axis=1)297dataFirstTB1x = dataFirstTB1.iloc[:, :-1]298dataFirstTB1Y = dataFirstTB1['Y']299model_0 = LassoCV()300model_1 = LassoCV()301model_0.fit(dataFirstTB0x, dataFirstTB0Y)302model_1.fit(dataFirstTB1x, dataFirstTB1Y)303prediction0 = model_0.predict(dataFirstTBx)304prediction1 = model_1.predict(dataFirstTBx)305ATE_TLearner2 = float((prediction1-prediction0).sum()/len(dataFirstTBx))306#S-Learner307dataFirstTBX = dataFirstTB.iloc[:, :-1]308dataFirstTBY = dataFirstTB['Y']309modelS = Ridge()310modelS.fit(dataFirstTBX, dataFirstTBY)311#T=1312dummT1 = dataFirstTB[dataFirstTB["IW_Treatment"] == 1]313dummT1x = dataFirstTB.iloc[:, :-1]314predictionUn = modelS.predict(dummT1x)315dummT1["IW_Treatment"] = 0316dummT1x = dummT1.iloc[:, :-1]317predictionZe = modelS.predict(dummT1x)318ATE_SLearner2 = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))319# ---------matching---------320#T=0321dummT0 = dataFirstTB[dataFirstTB["IW_Treatment"] == 0]322dummT0 = dummT0.drop(["IW_Treatment"], axis=1)323dummT0x = dummT0.iloc[:, :-1]324dummT0Y = dummT0['Y']325#T=1326dummT1 = dataFirstTB[dataFirstTB["IW_Treatment"] == 1]327dummT1 = dummT1.drop(["IW_Treatment"], axis=1)328dummT1x = dummT1.iloc[:, :-1]329dummT1Y = dummT1['Y']330dummT0V = dummT0.values331dummT1V = dummT1.values332dummT1XV=dummT1x.values333dummT0XV=dummT0x.values334ITE = []335for row in dummT1V:336 dif=abs(row[:-1]-dummT0V[:,:-1])337 #index338 #dif.argmin()339 #value340 distance=row[-1]-dummT0V[dif.argmin(),-1]341 ITE.append(distance)342#Sum of the Treated/number of343ATE_Matchingb2 = float(sum(ITE))/float(len(dataFirstTB))344#--------345corralation=dataFirstTB.corr()346res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[[\347 'invite_type', 'engagement_skill','target_skill','region','city','country','continent','user_os',\348 'browser','score','other_number_words',\349 'visitor_duration',\350 'agent_duration', 'visitor_number_words', 'agent_number_words',\351 'total_canned_lines', 'average_sent', 'min_sent', 'max_sent', 'n_sent_pos', 'n_sent_neg', 'first_sent',\352 'last_sent', 'id_rep_code', \353 'Invitation_Acep_Day_of_week', 'Invitation_Acep_Hour', \354 \355 ]],\356 dataFirstTB.IW_Treatment,dataFirstTB.NumberofAssigned).fit(cov_type='unadjusted')357print(res_second)358covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)359#-0.12360corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\361 'engagement_skill','target_skill']].corr()362#treatment 3 Invitation type363DataForThirdTreatment = pd.read_csv('DataForThirdTreatment.csv', index_col=0)364modelP3 = LogisticRegression(max_iter=10000)365ThirdX = DataForThirdTreatment[[\366 'region','city','country','continent','user_os', 'browser', 'score',\367 'Arrival_Day_of_week','Arrival_Hour',\368 'LOS_in_website_before_invorRequest',\369 ]]370ThirdY = DataForThirdTreatment.InvT371modelP3.fit(ThirdX, ThirdY)372Third_Propensityscore = np.asarray(modelP3.predict_proba(ThirdX))373treatment_plt3=plt.hist(Third_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')374cont_plt3=plt.hist(Third_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')375plt.legend();376plt.xlabel('propensity score');377plt.ylabel('number of units');378plt.savefig('prop_score_Inv.pdf',format='pdf')379ThirdIndex=\380np.where((Third_Propensityscore[:,1] < 0.8)& (Third_Propensityscore[:,1] > 0.25))381Third_PropensityscoreB=\382Third_Propensityscore[ThirdIndex[0]]383treatment_plt3B=plt.hist(Third_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')384cont_plt3B=plt.hist(Third_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')385plt.legend();386plt.xlabel('propensity score');387plt.ylabel('number of units');388plt.savefig('prop_score_Inv2.pdf',format='pdf')389datathird=DataForThirdTreatment[[\390 'region','city','country','continent','user_os', 'browser', 'score',\391 'Arrival_Day_of_week','Arrival_Hour',\392 'LOS_in_website_before_invorRequest',\393 'InvT','Y']]394dataFirstT2=datathird.iloc[ThirdIndex[0]]395len(dataFirstT2) #6387396len(dataFirstT) #23383397ATE_IPW=(((dataFirstT2['InvT']*dataFirstT2['Y'])/(Third_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\398-( ( ( (1-dataFirstT2['InvT'])*dataFirstT2['Y'] )/(Third_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))399dataFirstT.groupby("Y")["queue_sec"].mean()400#Out[132]:401#Y402#0 70.728748403#1 48.358257404dataFirstTB0 = datathird[datathird["InvT"] == 0]405dataFirstTB0 = dataFirstTB0.drop(["InvT"], axis=1)406dataFirstTB0x = dataFirstTB0.iloc[:, :-1]407dataFirstTB0Y = dataFirstTB0['Y']408dataFirstTBx = datathird.iloc[:, :-1]409dataFirstTBx = dataFirstTBx.drop(["InvT"], axis=1)410dataFirstTB1 = datathird[datathird["InvT"] == 1]411dataFirstTB1 = dataFirstTB1.drop(["InvT"], axis=1)412dataFirstTB1x = dataFirstTB1.iloc[:, :-1]413dataFirstTB1Y = dataFirstTB1['Y']414model_0 = LinearRegression()415model_1 = LinearRegression()416model_0.fit(dataFirstTB0x, dataFirstTB0Y)417model_1.fit(dataFirstTB1x, dataFirstTB1Y)418prediction0 = model_0.predict(dataFirstTBx)419prediction1 = model_1.predict(dataFirstTBx)420ATE_TLearner3 = float((prediction1-prediction0).sum()/len(dataFirstTBx))421#S-Learner422dataFirstTBX = datathird.iloc[:, :-1]423dataFirstTBY = datathird['Y']424modelS = Ridge()425modelS.fit(dataFirstTBX, dataFirstTBY)426#T=1427dummT1 = datathird[datathird["InvT"] == 1]428dummT1x = datathird.iloc[:, :-1]429predictionUn = modelS.predict(dummT1x)430dummT1["InvT"] = 0431dummT1x = dummT1.iloc[:, :-1]432predictionZe = modelS.predict(dummT1x)...

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OWTiii.py

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1import numpy as np2from sklearn.linear_model import LogisticRegression3import statsmodels.formula.api as smf4import matplotlib5matplotlib.use("TkAgg")6from matplotlib import pyplot as plt7from scipy.special import expit8#matplotlib inline9from sklearn.linear_model import LassoCV10from sklearn.linear_model import Ridge11from sklearn.linear_model import LinearRegression12import statsmodels.api as sm13from scipy import stats14from linearmodels.iv import IV2SLS15import os, glob16import pandas as pd17#FIRST TREATMENT iii18dataFirstT = pd.read_csv('DataForFirstTreatmentiii.csv', index_col=0)19len(dataFirstT)#12,34320dataFirstT.WaitTreatment\21 = np.where(dataFirstT['queue_sec']< 30, 1, 0)22dataFirstT.WaitTreatment\23 = np.where(dataFirstT['queue_sec']< 60, 1, 0)24dFT=dataFirstT[[\25'invite_type','engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\26]]27modelP = LogisticRegression(max_iter=10000)28FirstX = dataFirstT[[\29 'Invitation_Acep_Hour', \30 'Rho_atarrival', 'invite_type', \31 'engagement_skill', 'target_skill',\32 'Invitation_Acep_Day_of_week',\33 'score','region','city','country','continent',\34 'user_os','browser'\35 ]]36#Wait time37FirstY = dataFirstT.WaitTreatment38modelP.fit(FirstX, FirstY)39reg2 = LinearRegression().fit(FirstX, FirstY)40First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))41treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')42cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')43plt.legend();44plt.xlabel('propensity score');45plt.ylabel('number of units');46plt.savefig('prop_score_wait30iii.pdf',format='pdf')47#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])48FirstIndex=\49np.where((First_Propensityscore[:, 1] < 0.55)& (First_Propensityscore[:, 1] > 0.46))50First_PropensityscoreB=\51First_Propensityscore[FirstIndex[0]]52treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')53cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')54plt.legend();55plt.xlabel('propensity score');56plt.ylabel('number of units');57plt.savefig('prop_score_wait302iii.pdf',format='pdf')58dataFirstT2=dFT.iloc[FirstIndex[0]]59len(dataFirstT2) #30 sec #1295 #60 sec #4,67560len(dataFirstT) #2338361ATE_IPWii=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\62-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))63dataFirstT.groupby("Y")["queue_sec"].mean()64#Out[132]:65#Y66#0 70.72874867#1 48.35825768# ----- T-learner --------69#dataFirstTB = dataFirstT2[[\70# 'Invitation_Acep_Hour', \71# 'Rho_atarrival', 'invite_type', \72# 'engagement_skill', 'target_skill',\73# 'WaitTreatment', 'Y']]74dataFirstTB=dataFirstT275dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]76dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)77dataFirstTB0x = dataFirstTB0.iloc[:, :-1]78dataFirstTB0Y = dataFirstTB0['Y']79dataFirstTBx = dataFirstTB.iloc[:, :-1]80dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)81dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]82dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)83dataFirstTB1x = dataFirstTB1.iloc[:, :-1]84dataFirstTB1Y = dataFirstTB1['Y']85model_0 = LassoCV()86model_1 = LassoCV()87model_0.fit(dataFirstTB0x, dataFirstTB0Y)88model_1.fit(dataFirstTB1x, dataFirstTB1Y)89prediction0 = model_0.predict(dataFirstTBx)90prediction1 = model_1.predict(dataFirstTBx)91ATE_TLearnerii = float((prediction1-prediction0).sum()/len(dataFirstTBx))92# ----- T-learner --------93# ------- S-Learner ---------94dataFirstTBX = dataFirstTB.iloc[:, :-1]95dataFirstTBY = dataFirstTB['Y']96modelS = Ridge()97modelS.fit(dataFirstTBX, dataFirstTBY)98#T=199dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]100dummT1x = dataFirstTB.iloc[:, :-1]101predictionUn = modelS.predict(dummT1x)102dummT1["WaitTreatment"] = 0103dummT1x = dummT1.iloc[:, :-1]104predictionZe = modelS.predict(dummT1x)105ATE_SLearnerii = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))106# ------- S-Learner ---------107# ---------matching---------108#T=0109dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]110dummT0 = dummT0.drop(["WaitTreatment"], axis=1)111dummT0x = dummT0.iloc[:, :-1]112dummT0Y = dummT0['Y']113#T=1114dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]115dummT1 = dummT1.drop(["WaitTreatment"], axis=1)116dummT1x = dummT1.iloc[:, :-1]117dummT1Y = dummT1['Y']118dummT0V = dummT0.values119dummT1V = dummT1.values120dummT1XV=dummT1x.values121dummT0XV=dummT0x.values122ITE = []123for row in dummT1V:124 dif=abs(row[:-1]-dummT0V[:,:-1]).sum(axis=1)125 #index126 #dif.argmin()127 #value128 distance=row[-1]-dummT0V[dif.argmin(),-1]129 ITE.append(distance)130#Sum of the Treated/number of Treated131ATE_Matchingbii = float(sum(ITE))/float(len(dataFirstTB))132# ---------matching---------133#-----------IV's-------------134corralation=dataFirstTB.corr()135res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['invite_type','engagement_skill','Rho_atarrival','region','city','country','continent','user_os','browser','score','Invitation_Acep_Hour']],\136 dataFirstTB.WaitTreatment, dataFirstTB.Invitation_Acep_Day_of_week).fit(cov_type='unadjusted')137print(res_second)138covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)139#-0.12140corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\141 'engagement_skill','target_skill']].corr()142#-----------IV's-------------143#FIRST TREATMENT ii -30 sec144dataFirstT = pd.read_csv('DataForFirstTreatmentii.csv', index_col=0)145dataFirstT.WaitTreatment\146 = np.where(dataFirstT['queue_sec']< 30, 1, 0)147len(dataFirstT)#12,343148dFT=dataFirstT[[\149'invite_type','engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\150]]151modelP = LogisticRegression(max_iter=10000)152FirstX = dataFirstT[[\153 'Invitation_Acep_Hour', \154 'Rho_atarrival', 'invite_type', \155 'engagement_skill', 'target_skill',\156 'Invitation_Acep_Day_of_week',\157 'score','region','city','country','continent',\158 'user_os','browser'\159 ]]160#Wait time161FirstY = dataFirstT.iloc[:, -2]162modelP.fit(FirstX, FirstY)163reg2 = LinearRegression().fit(FirstX, FirstY)164First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))165treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')166cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')167plt.legend();168plt.xlabel('propensity score');169plt.ylabel('number of units');170plt.savefig('prop_score_waiti2.pdf',format='pdf')171#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])172FirstIndex=\173np.where((First_Propensityscore[:, 1] < 0.60)& (First_Propensityscore[:, 1] > 0.45))174First_PropensityscoreB=\175First_Propensityscore[FirstIndex[0]]176treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')177cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')178plt.legend();179plt.xlabel('propensity score');180plt.ylabel('number of units');181plt.savefig('prop_score_wait2ii.pdf',format='pdf')182dataFirstT2=dFT.iloc[FirstIndex[0]]183len(dataFirstT2) #4,667184len(dataFirstT) #23383185ATE_IPWii=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\186-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))187dataFirstT.groupby("Y")["queue_sec"].mean()188#Out[132]:189#Y190#0 70.728748191#1 48.358257192# ----- T-learner --------193#dataFirstTB = dataFirstT2[[\194# 'Invitation_Acep_Hour', \195# 'Rho_atarrival', 'invite_type', \196# 'engagement_skill', 'target_skill',\197# 'WaitTreatment', 'Y']]198dataFirstTB=dataFirstT2199dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]200dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)201dataFirstTB0x = dataFirstTB0.iloc[:, :-1]202dataFirstTB0Y = dataFirstTB0['Y']203dataFirstTBx = dataFirstTB.iloc[:, :-1]204dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)205dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]206dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)207dataFirstTB1x = dataFirstTB1.iloc[:, :-1]208dataFirstTB1Y = dataFirstTB1['Y']209model_0 = LassoCV()210model_1 = LassoCV()211model_0.fit(dataFirstTB0x, dataFirstTB0Y)212model_1.fit(dataFirstTB1x, dataFirstTB1Y)213prediction0 = model_0.predict(dataFirstTBx)214prediction1 = model_1.predict(dataFirstTBx)215ATE_TLearnerii = float((prediction1-prediction0).sum()/len(dataFirstTBx))216# ----- T-learner --------217# ------- S-Learner ---------218dataFirstTBX = dataFirstTB.iloc[:, :-1]219dataFirstTBY = dataFirstTB['Y']220modelS = Ridge()221modelS.fit(dataFirstTBX, dataFirstTBY)222#T=1223dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]224dummT1x = dataFirstTB.iloc[:, :-1]225predictionUn = modelS.predict(dummT1x)226dummT1["WaitTreatment"] = 0227dummT1x = dummT1.iloc[:, :-1]228predictionZe = modelS.predict(dummT1x)229ATE_SLearnerii = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))230# ------- S-Learner ---------231# ---------matching---------232#T=0233dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]234dummT0 = dummT0.drop(["WaitTreatment"], axis=1)235dummT0x = dummT0.iloc[:, :-1]236dummT0Y = dummT0['Y']237#T=1238dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]239dummT1 = dummT1.drop(["WaitTreatment"], axis=1)240dummT1x = dummT1.iloc[:, :-1]241dummT1Y = dummT1['Y']242dummT0V = dummT0.values243dummT1V = dummT1.values244dummT1XV=dummT1x.values245dummT0XV=dummT0x.values246ITE = []247for row in dummT1V:248 dif=abs(row[:-1]-dummT0V[:,:-1]).sum(axis=1)249 #index250 #dif.argmin()251 #value252 distance=row[-1]-dummT0V[dif.argmin(),-1]253 ITE.append(distance)254#Sum of the Treated/number of Treated255ATT_Matchingbii = float(sum(ITE))/float(len(dummT1V))256# ---------matching---------257#-----------IV's-------------258corralation=dataFirstTB.corr()259res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour']],\260 dataFirstTB.WaitTreatment, dataFirstTB.Rho_atarrival).fit(cov_type='unadjusted')261print(res_second)262covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)263#-0.12264corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\265 'engagement_skill','target_skill']].corr()...

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OWTii.py

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1import numpy as np2from sklearn.linear_model import LogisticRegression3import statsmodels.formula.api as smf4import matplotlib5matplotlib.use("TkAgg")6from matplotlib import pyplot as plt7from scipy.special import expit8#matplotlib inline9from sklearn.linear_model import LassoCV10from sklearn.linear_model import Ridge11from sklearn.linear_model import LinearRegression12import statsmodels.api as sm13from scipy import stats14from linearmodels.iv import IV2SLS15import os, glob16import pandas as pd17#FIRST TREATMENT ii18dataFirstT = pd.read_csv('DataForFirstTreatmentii.csv', index_col=0)19len(dataFirstT)#12,34320dataFirstT.WaitTreatment\21 = np.where(dataFirstT['queue_sec']< 30, 1, 0)22dataFirstT.WaitTreatment\23 = np.where(dataFirstT['queue_sec']< 60, 1, 0)24dFT=dataFirstT[[\25'invite_type','engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\26]]27modelP = LogisticRegression(max_iter=10000)28FirstX = dataFirstT[[\29 'Invitation_Acep_Hour', \30 'Rho_atarrival', 'invite_type', \31 'engagement_skill', 'target_skill',\32 'Invitation_Acep_Day_of_week',\33 'score','region','city','country','continent',\34 'user_os','browser'\35 ]]36#Wait time37FirstY = dataFirstT.iloc[:, -2]38modelP.fit(FirstX, FirstY)39reg2 = LinearRegression().fit(FirstX, FirstY)40First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))41treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')42cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')43plt.legend();44plt.xlabel('propensity score');45plt.ylabel('number of units');46plt.savefig('prop_score_wait60ii.pdf',format='pdf')47#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])48FirstIndex=\49np.where((First_Propensityscore[:, 1] < 0.55)& (First_Propensityscore[:, 1] > 0.46))50First_PropensityscoreB=\51First_Propensityscore[FirstIndex[0]]52treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')53cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')54plt.legend();55plt.xlabel('propensity score');56plt.ylabel('number of units');57plt.savefig('prop_score_wait602ii.pdf',format='pdf')58dataFirstT2=dFT.iloc[FirstIndex[0]]59len(dataFirstT2) #30 sec #1295 #60 sec #4,67560len(dataFirstT) #2338361ATE_IPWii=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\62-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))63dataFirstT.groupby("Y")["queue_sec"].mean()64#Out[132]:65#Y66#0 70.72874867#1 48.35825768# ----- T-learner --------69#dataFirstTB = dataFirstT2[[\70# 'Invitation_Acep_Hour', \71# 'Rho_atarrival', 'invite_type', \72# 'engagement_skill', 'target_skill',\73# 'WaitTreatment', 'Y']]74dataFirstTB=dataFirstT275dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]76dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)77dataFirstTB0x = dataFirstTB0.iloc[:, :-1]78dataFirstTB0Y = dataFirstTB0['Y']79dataFirstTBx = dataFirstTB.iloc[:, :-1]80dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)81dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]82dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)83dataFirstTB1x = dataFirstTB1.iloc[:, :-1]84dataFirstTB1Y = dataFirstTB1['Y']85model_0 = LassoCV()86model_1 = LassoCV()87model_0.fit(dataFirstTB0x, dataFirstTB0Y)88model_1.fit(dataFirstTB1x, dataFirstTB1Y)89prediction0 = model_0.predict(dataFirstTBx)90prediction1 = model_1.predict(dataFirstTBx)91ATE_TLearnerii = float((prediction1-prediction0).sum()/len(dataFirstTBx))92# ----- T-learner --------93# ------- S-Learner ---------94dataFirstTBX = dataFirstTB.iloc[:, :-1]95dataFirstTBY = dataFirstTB['Y']96modelS = Ridge()97modelS.fit(dataFirstTBX, dataFirstTBY)98#T=199dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]100dummT1x = dataFirstTB.iloc[:, :-1]101predictionUn = modelS.predict(dummT1x)102dummT1["WaitTreatment"] = 0103dummT1x = dummT1.iloc[:, :-1]104predictionZe = modelS.predict(dummT1x)105ATE_SLearnerii = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))106# ------- S-Learner ---------107# ---------matching---------108#T=0109dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]110dummT0 = dummT0.drop(["WaitTreatment"], axis=1)111dummT0x = dummT0.iloc[:, :-1]112dummT0Y = dummT0['Y']113#T=1114dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]115dummT1 = dummT1.drop(["WaitTreatment"], axis=1)116dummT1x = dummT1.iloc[:, :-1]117dummT1Y = dummT1['Y']118dummT0V = dummT0.values119dummT1V = dummT1.values120dummT1XV=dummT1x.values121dummT0XV=dummT0x.values122ITE = []123for row in dummT1V:124 dif=abs(row[:-1]-dummT0V[:,:-1]).sum(axis=1)125 #index126 #dif.argmin()127 #value128 distance=row[-1]-dummT0V[dif.argmin(),-1]129 ITE.append(distance)130#Sum of the Treated/number of Treated131ATE_Matchingbii = float(sum(ITE))/float(len(dataFirstTB))132# ---------matching---------133#-----------IV's-------------134corralation=dataFirstTB.corr()135res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['invite_type','engagement_skill','Rho_atarrival','region','city','country','continent','user_os','browser','score','Invitation_Acep_Hour']],\136 dataFirstTB.WaitTreatment, dataFirstTB.Invitation_Acep_Day_of_week).fit(cov_type='unadjusted')137print(res_second)138covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)139#-0.12140corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\141 'engagement_skill','target_skill']].corr()142#-----------IV's-------------143#FIRST TREATMENT ii -30 sec144dataFirstT = pd.read_csv('DataForFirstTreatmentii.csv', index_col=0)145dataFirstT.WaitTreatment\146 = np.where(dataFirstT['queue_sec']< 30, 1, 0)147len(dataFirstT)#12,343148dFT=dataFirstT[[\149'invite_type','engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour','Rho_atarrival','WaitTreatment','Y'\150]]151modelP = LogisticRegression(max_iter=10000)152FirstX = dataFirstT[[\153 'Invitation_Acep_Hour', \154 'Rho_atarrival', 'invite_type', \155 'engagement_skill', 'target_skill',\156 'Invitation_Acep_Day_of_week',\157 'score','region','city','country','continent',\158 'user_os','browser'\159 ]]160#Wait time161FirstY = dataFirstT.iloc[:, -2]162modelP.fit(FirstX, FirstY)163reg2 = LinearRegression().fit(FirstX, FirstY)164First_Propensityscore = np.asarray(modelP.predict_proba(FirstX))165treatment_plt=plt.hist(First_Propensityscore[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')166cont_plt=plt.hist(First_Propensityscore[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')167plt.legend();168plt.xlabel('propensity score');169plt.ylabel('number of units');170plt.savefig('prop_score_waiti2.pdf',format='pdf')171#hist, bin_edges = np.histogram(First_Propensityscore[:, 1])172FirstIndex=\173np.where((First_Propensityscore[:, 1] < 0.60)& (First_Propensityscore[:, 1] > 0.45))174First_PropensityscoreB=\175First_Propensityscore[FirstIndex[0]]176treatment_plt1B=plt.hist(First_PropensityscoreB[:, 1],fc=(0,0,1,0.5),bins=10,label='Treated')177cont_plt1B=plt.hist(First_PropensityscoreB[:, 0],fc=(1,0,0,0.5),bins=10,label='Control')178plt.legend();179plt.xlabel('propensity score');180plt.ylabel('number of units');181plt.savefig('prop_score_wait2ii.pdf',format='pdf')182dataFirstT2=dFT.iloc[FirstIndex[0]]183len(dataFirstT2) #4,667184len(dataFirstT) #23383185ATE_IPWii=(((dataFirstT2['WaitTreatment']*dataFirstT2['Y'])/(First_PropensityscoreB[:, 1])).sum())*(1/len(dataFirstT2))\186-( ( ( (1-dataFirstT2['WaitTreatment'])*dataFirstT2['Y'] )/(First_PropensityscoreB[:, 0]) ).sum())*(1/len(dataFirstT2))187dataFirstT.groupby("Y")["queue_sec"].mean()188#Out[132]:189#Y190#0 70.728748191#1 48.358257192# ----- T-learner --------193#dataFirstTB = dataFirstT2[[\194# 'Invitation_Acep_Hour', \195# 'Rho_atarrival', 'invite_type', \196# 'engagement_skill', 'target_skill',\197# 'WaitTreatment', 'Y']]198dataFirstTB=dataFirstT2199dataFirstTB0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]200dataFirstTB0 = dataFirstTB0.drop(["WaitTreatment"], axis=1)201dataFirstTB0x = dataFirstTB0.iloc[:, :-1]202dataFirstTB0Y = dataFirstTB0['Y']203dataFirstTBx = dataFirstTB.iloc[:, :-1]204dataFirstTBx = dataFirstTBx.drop(["WaitTreatment"], axis=1)205dataFirstTB1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]206dataFirstTB1 = dataFirstTB1.drop(["WaitTreatment"], axis=1)207dataFirstTB1x = dataFirstTB1.iloc[:, :-1]208dataFirstTB1Y = dataFirstTB1['Y']209model_0 = LassoCV()210model_1 = LassoCV()211model_0.fit(dataFirstTB0x, dataFirstTB0Y)212model_1.fit(dataFirstTB1x, dataFirstTB1Y)213prediction0 = model_0.predict(dataFirstTBx)214prediction1 = model_1.predict(dataFirstTBx)215ATE_TLearnerii = float((prediction1-prediction0).sum()/len(dataFirstTBx))216# ----- T-learner --------217# ------- S-Learner ---------218dataFirstTBX = dataFirstTB.iloc[:, :-1]219dataFirstTBY = dataFirstTB['Y']220modelS = Ridge()221modelS.fit(dataFirstTBX, dataFirstTBY)222#T=1223dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]224dummT1x = dataFirstTB.iloc[:, :-1]225predictionUn = modelS.predict(dummT1x)226dummT1["WaitTreatment"] = 0227dummT1x = dummT1.iloc[:, :-1]228predictionZe = modelS.predict(dummT1x)229ATE_SLearnerii = (predictionUn.sum() / len(dummT1)) - (predictionZe.sum() / len(dummT1))230# ------- S-Learner ---------231# ---------matching---------232#T=0233dummT0 = dataFirstTB[dataFirstTB["WaitTreatment"] == 0]234dummT0 = dummT0.drop(["WaitTreatment"], axis=1)235dummT0x = dummT0.iloc[:, :-1]236dummT0Y = dummT0['Y']237#T=1238dummT1 = dataFirstTB[dataFirstTB["WaitTreatment"] == 1]239dummT1 = dummT1.drop(["WaitTreatment"], axis=1)240dummT1x = dummT1.iloc[:, :-1]241dummT1Y = dummT1['Y']242dummT0V = dummT0.values243dummT1V = dummT1.values244dummT1XV=dummT1x.values245dummT0XV=dummT0x.values246ITE = []247for row in dummT1V:248 dif=abs(row[:-1]-dummT0V[:,:-1]).sum(axis=1)249 #index250 #dif.argmin()251 #value252 distance=row[-1]-dummT0V[dif.argmin(),-1]253 ITE.append(distance)254#Sum of the Treated/number of Treated255ATT_Matchingbii = float(sum(ITE))/float(len(dummT1V))256# ---------matching---------257#-----------IV's-------------258corralation=dataFirstTB.corr()259res_second = IV2SLS(dataFirstTB.Y,dataFirstTB[['engagement_skill','target_skill','region','city','country','continent','user_os','browser','score','Invitation_Acep_Day_of_week','Invitation_Acep_Hour']],\260 dataFirstTB.WaitTreatment, dataFirstTB.Rho_atarrival).fit(cov_type='unadjusted')261print(res_second)262covariance(dataFirstT.queue_sec,dataFirstT.Rho_atarrival)263#-0.12264corralation=dataFirstT[['queue_sec','Rho_atarrival','invite_type',\265 'engagement_skill','target_skill']].corr()...

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parse_table.py

Source:parse_table.py Github

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1from ll1 import log2'''LL(1) Parse Table3# Constructs an LL(1) parse table from a given Grammer.4 The table is map of nonterminal maps. Table = { nonterminal : { first, rule} }5 Each nonterminal is mapped to a rule by one of it's firsts.6 Allows rules access as table[terminal][nonterminal]7 If terminal is not in table[nonterminal] then reject terminal8 9 Remember, if two distinct rules are mapped from table[nonterminal][terminal]10 then the grammer is not LL(1) !!11# Grammer : map with each nonterminal key is mapped to a list of production rules12# Rules : an in order list of terminals and nonterminals.13 14# Terminals : symbols that are not mapped to production rules15# Epsilon : a special terminal that allows nonterminal to terminal 16 17# Symbols : the tag used to identify token tags(terminals) and rule tags (nonterminals)18 19# First set : the set of possible first terminals that match with a production rule for a nonterminal.20 If a terminal parsed is a e, and e is also within the first set of S.21 For each rule for a nonterminal, each rule's first symbol22 If the first symbol is a terminal then add it to first set23 If the first symbol is a nonterminal and not equal to first set's nonterminal24 then union the nonterminals first into first set25# Follow set : the set of terminals that follow a nonterminal symbol in any given production rule.26 For a nonterminal in rule r.27 If the nonterminal has nothing following it or follows EPSILON in rule, 28 then union the follow set of the nonterminal that is mapped to rule r,29 the parent symbol, if it is not the same.30 If the following symbol in rule is a nonterminal, 31 then union the follow set with the following nonterminal's first set. 32 If one of the firsts is epsilon, then union the follow set of parent symbol 33 By default START is followed by EOI34# Example Grammer and Table Constructed35 Nonterminals : S, E36 Terminals : a, +, EPSILON37 Grammer = 38 {39 E : [[T, A]],40 A : [[+, T, A],41 [EPSILON]42 ]43 T : [[a]]44 }45 46 first_set(E) = [a] # first set of T47 first_set(A) = [+, EPSILON] # + and epsilon are terminal so add 48 first_set(T) = [a] # a is terminal so add49 follow_set(E) = [EOI] # is start so default follows EOI 50 follow_set(A) = [EOI] # A follows nothing in each rule, so union the follow of rules symbol that is not A,51 so follow set of E 52 follow_set(T) = [+, EOI] # T is followed by A, so follow first set of A, however, epsilon is in first so union53 # follow set of A54 Table =55 {56 E : { a : [T, A], }57 A : { + : [T, A], EOI : [EPSILON] }58 T : { a : [a] }59 }60 | a | + | EOI61 ----------------------62 E | T A | |63 A | | T A | EPSILON64 T | a | |65'''66class ll1_parse_table(dict): 67 # Inherits dict to allow access to rule for table[nonterminal][terminal]68 def __init__(self, grammer, start_sym, epsilon_sym):69 self.grammer = grammer70 #predefined parsing symbols each self.grammer must use71 self.START = start_sym72 self.EPSILON = epsilon_sym73 self.EOI = 'EOI'# default end of input symbol74 self.construct()75 76 #returns string representation of table77 def __repr__(self):78 table = 'Parse Table\n'79 for symbol in self.keys():80 table += symbol + ':\n'81 for first in self[symbol].keys():82 rule = ''83 line = '\t' + '{:<10}'.format(first+ ':')84 for r in self[symbol][first]:85 rule += r + ' ' 86 table += line + '[' + rule + ']\n'87 return table88 # Constructs the LL(1) parse table grammers rules. 89 def construct(self):90 for symbol in self.grammer.keys():91 self[symbol] = {} # allocate map for symbol92 # find first and follow set93 firsts = self.first_set(symbol)94 follows = self.follow_set(symbol) 95 for first in firsts:96 # add rule whose first matches first of rule97 for rule in self.grammer[symbol]: # for each rule in symbols rule list98 if first in self.first_set(rule[0]): # if the first matches the first of the rule99 if first not in self[symbol]: # if symbol, first rule has not been added 100 if first == self.EPSILON: # get follow of parent symbol if first is epsilon101 # for each follow, assign epsilon102 for follow in follows:103 self[symbol][follow] = [first] # make epsilon symbol a rule (list)104 else:105 self[symbol][first] = rule106 else:107 log.error('TABLE: Duplicate Rules for ' + symbol + ',' + first)108 # returns list of possible symbols that are the first symbol in the rule109 def first_set(self, symbol):110 firsts = []111 if symbol in self.grammer: ## if symbols rules exits112 rule_set = self.grammer[symbol]113 for rule in rule_set: # check each rule. Rule is list of inorder symbols114 # if rule's first symbol is not the same as root. add the first set 115 # of that symbol to this symbols first set116 if len(rule) > 0 and (rule[0] != symbol): 117 firsts = firsts + self.first_set(rule[0]) # concat first sets118 else: # no rules119 # the symbol is a terminal, so return it as is120 firsts.append(symbol)121 return firsts122 #returns list of symbols that can follow the given symbol123 def follow_set(self, symbol):124 follows = [] # set of follow symbols125 # End of input follows start by default 126 if symbol == self.START: 127 follows.append(self.EOI) 128 ''' search for symbol in every rule of every other symbol and find next possible129 terminal symbol that can follow current symbol130 iterate through rule set from each symbol'''131 for rule_symbol in self.grammer.keys(): 132 # iterate through each rule in rule set for each symbol133 for rule in self.grammer[rule_symbol]:134 i = 0 # index of the symbol in the rule135 # check every symbol in rule136 while i < len(rule): 137 if rule[i] == symbol: # if rule symbol is equal to symbol138 if i+1 < len(rule): # try to get the next following symbol in the rule139 if rule[i+1] not in self.grammer: # if next symbol is terminal 140 follows.append(rule[i+1]) # append rule symbol to follow set141 else: # else if next symbol is nonterminal, union nonterminal's firsts into follow142 for first in self.first_set(rule[i+1]): # for each first in the next symbols rule set 143 if first == self.EPSILON: # if the first is epsilon144 # union symbol's follow set with the follow set of the parent rule symbol 145 follows = follows + [x for x in iter(self.follow_set(rule_symbol)) if x not in follows]146 elif first not in follows: # if not epsilon and doesnt already exist in follow set147 follows.append(first) # append symbol to follow set148 # if at the end of rule(no next symbol), 149 elif rule_symbol != symbol:#and parent symbol is not the same as current symbol150 #union symbol's follow set with the follow set of the parent rule symbol151 follows = follows + [x for x in iter(self.follow_set(rule_symbol)) if x not in follows]152 i = i + 1 ...

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test_replace_child.py

Source:test_replace_child.py Github

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1from typing import Tuple2from hypothesis import given3from tests.utils import (BoundPortedNodesPair,4 are_bound_ported_nodes_equal,5 are_bound_ported_nodes_sequences_equal)6from . import strategies7@given(strategies.nested_nodes_with_children_pairs, strategies.nodes_pairs)8def test_basic(first_nodes_with_children_pair: Tuple[BoundPortedNodesPair,9 BoundPortedNodesPair],10 second_nodes_pair: BoundPortedNodesPair) -> None:11 first_nodes, first_children = first_nodes_with_children_pair12 first_bound, first_ported = first_nodes13 first_bound_child, first_ported_child = first_children14 second_bound, second_ported = second_nodes_pair15 first_bound.replace_child(first_bound_child, second_bound)16 first_ported.replace_child(first_ported_child, second_ported)17 assert are_bound_ported_nodes_sequences_equal(first_bound_child.parents,18 first_ported_child.parents)19 assert are_bound_ported_nodes_sequences_equal(second_bound.parents,20 second_ported.parents)...

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Using AI Code Generation

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1const { fc } = require("fast-check");2const fc = require("fast-check");3fc.assert(4 fc.property(fc.integer(), fc.integer(), (a, b) => {5 return a + b >= a;6 })7);8fc.assert(9 fc.property(fc.integer(), fc.integer(), (a, b) => {10 return a + b >= a;11 })12);13const { fc } = require("fast-check");14const fc = require("fast-check");15fc.assert(16 fc.property(fc.integer(), fc.integer(), (a, b) => {17 return a + b >= a;18 })19);20fc.assert(21 fc.property(fc.integer(), fc.integer(), (a, b) => {22 return a + b >= a;23 })24);25const { fc } = require("fast-check");26const fc = require("fast-check");27fc.assert(28 fc.property(fc.integer(), fc.integer(), (a, b) => {29 return a + b >= a;30 })31);32fc.assert(33 fc.property(fc.integer(), fc.integer(), (a, b) => {34 return a + b >= a;35 })36);37const { fc } = require("fast-check");38const fc = require("fast-check");39fc.assert(40 fc.property(fc.integer(), fc.integer(), (a, b) => {41 return a + b >= a;42 })43);44fc.assert(45 fc.property(fc.integer(), fc.integer(), (a, b) => {

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Using AI Code Generation

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1const fc = require('fast-check');2const myFirstMethod = require('fast-check-monorepo');3fc.assert(4 fc.property(fc.integer(), fc.integer(), (a, b) => {5 return myFirstMethod(a, b) === a + b;6 }),7);8const mySecondMethod = require('fast-check-monorepo/my-second-method');9fc.assert(10 fc.property(fc.integer(), fc.integer(), (a, b) => {11 return mySecondMethod(a, b) === a * b;12 }),13);14const myThirdMethod = require('fast-check-monorepo/my-third-method');15fc.assert(16 fc.property(fc.integer(), fc.integer(), (a, b) => {17 return myThirdMethod(a, b) === a - b;18 }),19);20const myFourthMethod = require('fast-check-monorepo/my-fourth-method');21fc.assert(22 fc.property(fc.integer(), fc.integer(), (a, b) => {23 return myFourthMethod(a, b) === a / b;24 }),25);26const myFifthMethod = require('fast-check-monorepo/my-fifth-method');27fc.assert(28 fc.property(fc.integer(), fc.integer(), (a, b) => {29 return myFifthMethod(a, b) === a % b;30 }),31);32const mySixthMethod = require('fast-check-monorepo/my-sixth-method');33fc.assert(34 fc.property(fc.integer(), fc.integer(), (a, b) => {35 return mySixthMethod(a, b) === a ** b;36 }),37);38const mySeventhMethod = require('fast-check-monorepo/my-seventh-method');39fc.assert(40 fc.property(fc.integer(), fc.integer(), (a, b) => {41 return mySeventhMethod(a, b) === a << b;42 }),43);44const myEighthMethod = require('fast-check-monorepo/my-eighth-method');45fc.assert(46 fc.property(fc.integer(), fc

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Using AI Code Generation

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1import {fc} from 'fast-check';2import {foo} from './foo';3import {fc} from 'fast-check/lib/fast-check-default';4import {foo} from './foo';5import {fc} from 'fast-check/lib/fast-check';6import {foo} from './foo';7import {fc} from 'fast-check/lib';8import {foo} from './foo';9fc.assert(10 fc.property(fc.integer(), fc.integer(), (a, b) => {11 return a + b === b + a;12 })13);14foo();15export function foo() {16 console.log('foo');17}18{19 "scripts": {20 },21 "dependencies": {22 }23}

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Using AI Code Generation

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1import * as fc from 'fast-check';2import { fc } from 'fast-check';3Cannot find module 'fast-check' or its corresponding type declarations.ts(2307)4Cannot find module 'fast-check' or its corresponding type declarations.ts(2307)5const first = <T>(arr: T[]) => arr[0];6const first = <T>(arr: T) => arr[0];7const first = <T>(arr: T) => arr[0];8const first = <T>(arr: T[]) => arr[0];9const last = <T>(arr: T[]) => arr[arr.length - 1];10const last = <T>(arr: T) => arr[arr.length - 1];11const last = <T>(arr: T) => arr[arr.length - 1];12const last = <T>(arr: T[]) => arr[arr.length - 1];13const second = <T>(arr: T[]) => arr[1];

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Using AI Code Generation

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1const fc = require('fast-check');2const {myFirstMethod} = require('fast-check-monorepo');3fc.assert(4 fc.property(fc.array(fc.integer(), 1, 100), (arr) => {5 const result = myFirstMethod(arr);6 return result > 0;7 })8);9const {mySecondMethod} = require('fast-check-monorepo');10fc.assert(11 fc.property(fc.array(fc.integer(), 1, 100), (arr) => {12 const result = mySecondMethod(arr);13 return result > 0;14 })15);

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Using AI Code Generation

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1const { check, property } = require('fast-check');2const { isEven } = require('./isEven');3const isEvenArbitrary = (min, max) => {4 return property(5 check.integer(min, max),6 (n) => isEven(n) === (n % 2 === 0)7 );8};9check(isEvenArbitrary(-1000, 1000));10const { check, property } = require('fast-check');11const isEven = (n) => {12 return n % 2 === 0;13};14const isEvenArbitrary = (min, max) => {15 return property(16 check.integer(min, max),17 (n) => isEven(n) === (n % 2 === 0)18 );19};20module.exports = { isEven, isEvenArbitrary };21const { isEvenArbitrary } = require('./isEven');22test('isEven', () => {23 fc.assert(isEvenArbitrary(-1000, 1000));24});25{26 "scripts": {27 },28 "dependencies": {29 },30 "devDependencies": {31 }32}

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Using AI Code Generation

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1import {fc} from 'fast-check';2import {arbitrary} from 'fast-check-monorepo';3const {Arbitrary} = fc;4const arbs = arbitrary(Arbitrary);5const arb = arbs.integer(0, 100);6fc.assert(fc.property(arb, (n) => n >= 0 && n <= 100));7import {fc} from 'fast-check';8import {integer} from 'fast-check-monorepo';9const arb = integer(0, 100);10fc.assert(fc.property(arb, (n) => n >= 0 && n <= 100));11"dependencies": {12 }13"dependencies": {14 }15"dependencies": {16 }17"dependencies": {18 }

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