How to use start_chunk method in Playwright Python

Best Python code snippet using playwright-python

organizer_copy.py

Source:organizer_copy.py

1import numpy as np 2from datetime import datetime as dt3from backports.datetime_fromisoformat import MonkeyPatch4MonkeyPatch.patch_fromisoformat()5MAX_PACKET_SIZE = 40966BYTES_IN_PACKET = 14567np.set_printoptions(threshold=np.inf,linewidth=325)8class Organizer:9	def __init__(self, all_data, num_chirp_loops, num_rx, num_tx, num_samples):10		self.data = all_data[0]11		self.packet_num = all_data[1]12		self.byte_count = all_data[2]13		self.num_packets = len(self.byte_count)14		self.num_chirps = num_chirp_loops*num_tx15		self.num_rx = num_rx16		self.num_samples = num_samples17		self.BYTES_IN_FRAME = self.num_chirps * self.num_rx * self.num_samples * 2 * 218		self.BYTES_IN_FRAME_CLIPPED = (self.BYTES_IN_FRAME // BYTES_IN_PACKET) * BYTES_IN_PACKET19		self.UINT16_IN_FRAME = self.BYTES_IN_FRAME // 220		self.NUM_PACKETS_PER_FRAME = self.BYTES_IN_FRAME // BYTES_IN_PACKET21		self.start_time = all_data[3]22		self.end_time = all_data[4]23	def iq(self, raw_frame):24		"""Reorganizes raw ADC data into a full frame25		Args:26			raw_frame (ndarray): Data to format27			num_chirps: Number of chirps included in the frame28			num_rx: Number of receivers used in the frame29			num_samples: Number of ADC samples included in each chirp30		Returns:31			ndarray: Reformatted frame of raw data of shape (num_chirps, num_rx, num_samples)32		"""33		ret = np.zeros(len(raw_frame) // 2, dtype=np.csingle)34		# Separate IQ data35		ret[0::2] = raw_frame[0::4] + 1j * raw_frame[2::4]36		ret[1::2] = raw_frame[1::4] + 1j * raw_frame[3::4]37		return ret.reshape((self.num_chirps, self.num_rx, self.num_samples))38	def get_frames(self, start_chunk, end_chunk, bc):39		# if first packet received is not the first byte transmitted40		if bc[start_chunk] == 0:41			bytes_left_in_curr_frame = 042			start = start_chunk*(BYTES_IN_PACKET // 2)43		else:44			frames_so_far = bc[start_chunk] // self.BYTES_IN_FRAME45			bytes_so_far = frames_so_far * self.BYTES_IN_FRAME46			# bytes_left_in_curr_frame = bc[start_chunk] - bytes_so_far47			bytes_left_in_curr_frame = (frames_so_far+1)*self.BYTES_IN_FRAME - bc[start_chunk]48			start = (bytes_left_in_curr_frame // 2) + start_chunk*(BYTES_IN_PACKET // 2)49		# print(start_chunk, start)50		# find num of frames51		total_bytes = bc[end_chunk] - (bc[start_chunk] + bytes_left_in_curr_frame)52		num_frames = total_bytes // (self.BYTES_IN_FRAME)53		# print(bc[end_chunk])54		# print(num_frames, start_chunk, end_chunk, self.BYTES_IN_FRAME)55		frames = np.zeros((num_frames, self.UINT16_IN_FRAME), dtype=np.int16)56		ret_frames = np.zeros((num_frames, self.num_chirps, self.num_rx, self.num_samples), dtype=complex)		57		# compress all received data into one byte stream58		all_uint16 = np.array(self.data).reshape(-1)59		# only choose uint16 starting from a new frame60		all_uint16 = all_uint16[start:]61		# organizing into frames62		for i in range(num_frames):63			frame_start_idx = i*self.UINT16_IN_FRAME64			frame_end_idx = (i+1)*self.UINT16_IN_FRAME65			frame = all_uint16[frame_start_idx:frame_end_idx]66			frames[i][:len(frame)] = frame.astype(np.int16)67			ret_frames[i] = self.iq(frames[i])	68		return ret_frames69	def organize(self):70		radar_unix_start_time = dt.timestamp(dt.fromisoformat(self.start_time[:-1]))*1e671		radar_unix_end_time = dt.timestamp(dt.fromisoformat(self.end_time[:-1]))*1e672		print('Start time: ', self.start_time)73		print('End time: ', self.end_time)74		self.byte_count = np.array(self.byte_count)75		self.data = np.array(self.data)76		self.packet_num = np.array(self.packet_num)77		# Reordering packets78		# sorted_idx = np.argsort(self.packet_num)79		# print(sorted_idx.dtype)80		# print(len(self.packet_num), len(self.byte_count), len(self.data), sorted_idx.shape)81		# self.packet_num = self.packet_num[sorted_idx]82		# self.data = self.data[sorted_idx]83		# self.byte_count = self.byte_count[sorted_idx]84		# self.packet_num = self.packet_num.tolist()85		# self.byte_count = self.byte_count.tolist()86		# self.data = self.data.tolist()87		bc = np.array(self.byte_count)88		packets_ooo = np.where(np.array(self.packet_num[1:])-np.array(self.packet_num[0:-1]) != 1)[0]89		is_not_monotonic = np.where(np.array(self.packet_num[1:])-np.array(self.packet_num[0:-1]) < 0)[0]90		print('Non monotonic packets: ', is_not_monotonic)91		if len(packets_ooo) == 0:92			print('packets in order')93			start_chunk = 094			ret_frames = self.get_frames(start_chunk, -1, bc)95		elif len(packets_ooo) == 1:96			print('1 packet not in order')97			start_chunk = packets_ooo[0]+198			ret_frames = self.get_frames(start_chunk, -1, bc)99			# start_chunk = 0100		else:101			print('Packet num not in order')102			packets_ooo = np.append(packets_ooo, len(self.packet_num)-1)103			# print('Packets ooo', packets_ooo)104			# print(self.NUM_PACKETS_PER_FRAME)105			# diff = [44]106			# for i in range(len(packets_ooo)-1):107			# 	# print(i, len(packets_ooo))108			# 	diff.append(self.packet_num[packets_ooo[i+1]]-self.packet_num[packets_ooo[i]+1])109			110			# print('Packets received before atleast 1 loss ', diff)111			# print('Total packets received ', np.sum(np.array(diff)))112			diff = []113			for i in range(len(packets_ooo)-1):114				diff.append(self.packet_num[packets_ooo[i]+1]-self.packet_num[packets_ooo[i]])115			116			# print('Packets lost before atleast 1 reception ', diff)117			packets_lost = np.sum(np.array(diff))118			packets_expected = self.packet_num[-1]-self.packet_num[0]+1119			print('Total packets lost ', packets_lost)120			print('Total packets expected ', packets_expected)121			print('Fraction lost ', packets_lost/packets_expected)122			new_packets_ooo = []123			start_new_packets_ooo = []124			end_new_packets_ooo = []125			for i in range(1, len(packets_ooo)):126				if (packets_ooo[i] - packets_ooo[i-1]) > self.NUM_PACKETS_PER_FRAME*2:127					new_packets_ooo.append(packets_ooo[i-1])128					start_new_packets_ooo.append(packets_ooo[i-1])129					end_new_packets_ooo.append(packets_ooo[i])130			new_packets_ooo = np.append(new_packets_ooo, -1)131			# print('New packets ooo', new_packets_ooo)132			# print('Start new packets ooo', start_new_packets_ooo)133			# print('End new packets ooo', end_new_packets_ooo)134			# exit()135			for i in range(len(start_new_packets_ooo)):136			# for i in range(len(new_packets_ooo)-1):137			# for i in [len(new_packets_ooo)-2]:138				# start_chunk = new_packets_ooo[i]+1139				# end_chunk = new_packets_ooo[i+1]140				start_chunk = start_new_packets_ooo[i]+1141				end_chunk = end_new_packets_ooo[i]142				# print(self.packet_num[start_chunk],self.packet_num[start_chunk-1])143				# print(self.byte_count[start_chunk],self.byte_count[start_chunk-1])144				curr_frames = self.get_frames(start_chunk, end_chunk, bc)145				if i == 0:146					ret_frames = curr_frames147				else:148					ret_frames = np.concatenate((ret_frames, curr_frames), axis=0)149		return ret_frames150		# Old approach151		# frame_start_idx = np.where((bc % self.BYTES_IN_FRAME_CLIPPED == 0) & (bc != 0))[0]152		# num_frames = len(frame_start_idx)-1153		# frames = np.zeros((num_frames, self.UINT16_IN_FRAME), dtype=np.int16)154		# ret_frames = np.zeros((num_frames, self.num_chirps, self.num_rx, self.num_samples), dtype=complex)155		# for i in range(num_frames):156		# 	d = np.array(self.data[frame_start_idx[i]:frame_start_idx[i+1]])157		# 	frame = d.reshape(-1)158		# 	frames[i][:len(frame)] = frame.astype(np.int16)159		# 	ret_frames[i] = self.iq(frames[i])...

pm_rmq.py

Source:pm_rmq.py

1import numpy as np2import math3import itertools4import random5#################################################6### Solution 1: O(1) query, O(n log n) space ####7#################################################8def preprocess_naive(arr):9    num_elem = len(arr)10    num_powers_of_2 = math.ceil(math.log(num_elem, 2)) + 111    solutions = np.full((num_elem, num_powers_of_2), -1)12    # Stor answer from every start point...13    for start_idx in range(num_elem):14        # ...for every interval lenth = power of 215        for i in range(num_powers_of_2):16            end_idx = start_idx + 2 ** i17            solutions[start_idx, i] = np.argmin(arr[start_idx : end_idx])18    return solutions19def query_naive(arr, solutions, start, end):20    assert(start != end)21    largest_pow = math.floor(math.log(end - start, 2))22    interval_size = 2 ** largest_pow23    start_1 = start24    start_2 = end - interval_size25    interval_1_argmin = start_1 + solutions[start_1, largest_pow]26    interval_2_argmin = start_2 + solutions[start_2, largest_pow]27    if arr[interval_1_argmin] <= arr[interval_2_argmin]:28        return interval_1_argmin29    else:30        return interval_2_argmin31def test_method_1():32    arr = [0, 1, 2, 1, 0, 1, 2, 3, 2, 3, 2, 1, 2, 1, 0, 1]33    print("Array:")34    print(arr)35    soln = preprocess_naive(arr)36    print("--------------")37    print("Preprocessing:")38    print(soln)39    print("--------------")40    print("Query (2, 5):", query_naive(arr, soln, 2, 5))41    for i in range(len(arr)):42        for j in range(i + 1, len(arr)):43            expected = i + np.argmin(arr[i:j])44            actual = query_naive(arr, soln, i, j)45            assert(expected == actual)46    print("All tests passed!")47###########################################48### Solution 2: O(1) query, O(n) space ####49###########################################50# Input:  numpy array51# Output: (chunk_size, num_chunks, top_array, bottom_array)52#         where top_array[i] is the argmin of ith chunk53#         and   bottom_array[i] is a view of a numpy array `l`54#               such that l[start, end] is the argmin of the 55#               values between `start' and `end' in the ith chunk56def pm_rmq_preprocess(arr):57    arr = np.array(arr)58    # 1) Split array into chunks of 1/2 lg n size59    chunk_size = math.floor(1/2 * math.log(len(arr), 2))60    num_chunks = math.ceil(len(arr)/chunk_size)61    # 2) Construct full lookup table62    #    * Enumerate all possible 2^{chunk_size} +- sqeuences63    lookup = np.zeros(shape = ((2, ) * (chunk_size - 1)) + (chunk_size, chunk_size + 1), dtype = int)64    for step_sequence in itertools.product([0,1], repeat = chunk_size - 1):65        sequence = np.zeros(shape = (chunk_size, ), dtype = int)66        for i in range(1, chunk_size):67            sequence[i] = sequence[i - 1] + (-1 if step_sequence[i - 1] == 0 else 1)68    69    #    * For each, compute the answers to all possible queries70        for start_query in range(0, chunk_size):71            for end_query in range(start_query + 1, chunk_size + 1):72                lookup_index = tuple(step_sequence) + (start_query, end_query)73                lookup[lookup_index] = np.argmin(sequence[start_query : end_query])74    # 3) Construct "top" array by brute force and "bottom" array of pointers75    chunk_summaries = np.zeros(num_chunks, dtype = int)76    bottom_lookup = [] # a list of *views* onto the full lookup table77    for i in range(num_chunks):78        start_chunk = i * chunk_size79        end_chunk = (i + 1) * chunk_size80        chunk = arr[start_chunk : end_chunk] - arr[start_chunk]81        chunk_sequence = [0 if d == -1 else 1 for d in np.diff(chunk)]82        chunk_summaries[i] = arr[start_chunk + np.argmin(chunk)]83        bottom_lookup.append(lookup[tuple(chunk_sequence)])84    # 4) Preprocess "top" array with O(n log n) space approach85    top_preprocessing = preprocess_naive(chunk_summaries)86    return (arr, chunk_summaries, top_preprocessing, bottom_lookup)87def pm_rmq_query(preprocessed_arr, start_index, end_index):88    arr, top, top_soln, bottom_lookup = preprocessed_arr89    chunk_size = math.floor(1/2 * math.log(len(arr), 2))90    num_chunks = math.ceil(len(arr)/chunk_size)91    start_chunk = start_index // chunk_size92    end_chunk = end_index // chunk_size93    start_within_chunk = start_index - start_chunk * chunk_size94    end_within_chunk = end_index - end_chunk * chunk_size95    if start_chunk == end_chunk:96        chunk_lookup = bottom_lookup[start_chunk]97        return start_index + chunk_lookup[start_within_chunk, end_within_chunk]98    else:99        # Start value100        start_lookup = bottom_lookup[start_chunk]101        argmin_start = start_index + start_lookup[start_within_chunk, -1]102        min_start = arr[argmin_start]103        # End value104        if end_within_chunk != 0:105            end_lookup = bottom_lookup[end_chunk]106            argmin_end = end_chunk * chunk_size + end_lookup[0, end_within_chunk]107            min_end = arr[argmin_end]108        else:109            argmin_end = -1110            min_end = np.inf111        # Intermediate values112        if start_chunk + 1 != end_chunk:113            argmin_chunk = query_naive(top, top_soln, start_chunk + 1, end_chunk)114            argmin_intermediate = argmin_chunk * chunk_size + bottom_lookup[argmin_chunk][0, -1]115            min_intermediate = arr[argmin_intermediate]116        else:117            argmin_intermediate = -1118            min_intermediate = np.inf119        # Return minimum value120        argmins = [argmin_start, argmin_intermediate, argmin_end]121        mins = [min_start, min_intermediate, min_end]122        return argmins[np.argmin(mins)]123def test_method_2():124    steps = [random.choice([0,1]) for _ in range(100)]125    arr = np.cumsum(steps)126    print("Array:")127    print(arr)128    preprocessed_arr = pm_rmq_preprocess(arr)129    q1 = pm_rmq_query(preprocessed_arr, 3, 10)130    print("Query [3, 10): ", q1)131    q2 = pm_rmq_query(preprocessed_arr, 5, 6)132    print("Query [5, 6): ", q2)133    134    for i in range(len(arr)):135        for j in range(i + 1, len(arr)):136            expected = i + np.argmin(arr[i:j])137            actual = pm_rmq_query(preprocessed_arr, i, j)138            assert(expected == actual)139    print("All tests passed!")140if __name__ == '__main__':...

salsa.py

Source:salsa.py

1from samson.utilities.manipulation import left_rotate, get_blocks2from samson.utilities.bytes import Bytes3from samson.core.primitives import StreamCipher, Primitive4from samson.core.metadata import SizeType, SizeSpec, EphemeralSpec, EphemeralType, ConstructionType, FrequencyType5from samson.ace.decorators import register_primitive6from copy import deepcopy7import math8def QUARTER_ROUND(a: int, b: int, c: int, d: int) -> (int, int, int, int):9    """10    Performs a quarter round of Salsa.11    Parameters:12        a (int): Salsa state variable.13        b (int): Salsa state variable.14        c (int): Salsa state variable.15        d (int): Salsa state variable.16    17    Returns:18        (int, int, int, int): New values for (a, b, c, d).19    """20    b = (b ^ left_rotate((a + d) & 0xFFFFFFFF,  7))21    c = (c ^ left_rotate((b + a) & 0xFFFFFFFF,  9))22    d = (d ^ left_rotate((c + b) & 0xFFFFFFFF, 13))23    a = (a ^ left_rotate((d + c) & 0xFFFFFFFF, 18))24    return a, b, c, d25@register_primitive()26class Salsa(StreamCipher):27    """28    Salsa stream cipher29    Add-rotate-xor (ARX) structure.30    https://en.wikipedia.org/wiki/Salsa2031    """32    CONSTRUCTION_TYPES = [ConstructionType.ADD_ROTATE_XOR]33    EPHEMERAL          = EphemeralSpec(ephemeral_type=EphemeralType.NONCE, size=SizeSpec(size_type=SizeType.SINGLE, sizes=96))34    USAGE_FREQUENCY    = FrequencyType.UNUSUAL35    def __init__(self, key: bytes, nonce: bytes, rounds: int=20, constant: bytes=b"expand 32-byte k"):36        """37        Parameters:38            key      (bytes): Key (128 or 256 bits).39            nonce    (bytes): Nonce (8 bytes).40            rounds     (int): Number of rounds to perform.41            constant (bytes): Constant used in generating the keystream (16 bytes).42        """43        Primitive.__init__(self)44        # If key is 80 bits, zero pad it (https://cr.yp.to/snuffle/salsafamily-20071225.pdf, 4.1)45        if len(key) == 10:46            key = Bytes.wrap(key).zfill(16)47        # If key is 128 bits, just repeat it48        if len(key) == 16:49            key += key50        self.key = key51        self.nonce = nonce52        self.rounds = rounds53        self.constant = constant54        self.counter = 055    def full_round(self, block_num: int, state: list=None) -> Bytes:56        """57        Performs a full round of Salsa.58        Parameters:59            block_num (int): Current block number.60        61        Returns:62            Bytes: Keystream block.63        """64        ctr_bytes = int.to_bytes(block_num, 8, 'little')65        cons_blocks  = [int.from_bytes(block, 'little') for block in get_blocks(self.constant, 4)]66        key_blocks   = [int.from_bytes(block, 'little') for block in get_blocks(self.key, 4)]67        ctr_blocks   = [int.from_bytes(block, 'little') for block in get_blocks(ctr_bytes, 4)]68        nonce_blocks = [int.from_bytes(block, 'little') for block in get_blocks(self.nonce, 4)]69        x = state or [70            cons_blocks[0],  *key_blocks[:4],71            cons_blocks[1],  *nonce_blocks,72            *ctr_blocks,     cons_blocks[2],73            *key_blocks[4:], cons_blocks[3]74        ]75        x = deepcopy(x)76        tmp = deepcopy(x)77        for _ in range(self.rounds // 2):78            # Odd round79            x[ 0], x[ 4], x[ 8], x[12] = QUARTER_ROUND(x[ 0], x[ 4], x[ 8], x[12])80            x[ 5], x[ 9], x[13], x[ 1] = QUARTER_ROUND(x[ 5], x[ 9], x[13], x[ 1])81            x[10], x[14], x[ 2], x[ 6] = QUARTER_ROUND(x[10], x[14], x[ 2], x[ 6])82            x[15], x[ 3], x[ 7], x[11] = QUARTER_ROUND(x[15], x[ 3], x[ 7], x[11])83            # Even round84            x[ 0], x[ 1], x[ 2], x[ 3] = QUARTER_ROUND(x[ 0], x[ 1], x[ 2], x[ 3])85            x[ 5], x[ 6], x[ 7], x[ 4] = QUARTER_ROUND(x[ 5], x[ 6], x[ 7], x[ 4])86            x[10], x[11], x[ 8], x[ 9] = QUARTER_ROUND(x[10], x[11], x[ 8], x[ 9])87            x[15], x[12], x[13], x[14] = QUARTER_ROUND(x[15], x[12], x[13], x[14])88        for i in range(16):89            x[i] += tmp[i]90        return Bytes(b''.join([int.to_bytes(state_int & 0xFFFFFFFF, 4, 'little') for state_int in x]), byteorder='little')91    def yield_state(self, start_chunk: int=0, num_chunks: int=1, state: list=None):92        """93        Generates `num_chunks` chunks of keystream starting from `start_chunk`.94        Parameters:95            num_chunks  (int): Desired number of 64-byte keystream chunks.96            start_chunk (int): Chunk number to start at.97            state      (list): Custom state to be directly injected.98        99        Returns:100            generator: Keystream chunks.101        """102        for iteration in range(start_chunk, start_chunk + num_chunks):103            yield self.full_round(iteration, state=state)104    def generate(self, length: int) -> Bytes:105        """106        Generates `length` of keystream.107        Parameters:108            length (int): Desired length of keystream in bytes.109        110        Returns:111            Bytes: Keystream.112        """113        num_chunks  = math.ceil(length / 64)114        start_chunk = self.counter // 64115        counter_mod = self.counter % 64116        if counter_mod:117            num_chunks += 1118        keystream = sum(list(self.yield_state(start_chunk=start_chunk, num_chunks=num_chunks)))[counter_mod:counter_mod+length]119        self.counter += length...

hpopt1.py

Source:hpopt1.py

1# -*- coding: utf-8 -*-2"""3Prepare data4numeric.to_hdf('numeric_b1_b7_nf149.hdf', 'numeric')5"""6import sys7sys.path.insert(0, '../../bosch_helper')8from bosch_helper import *9param_grid = {'max_depth': [13, 14, 15, 16], 10              'eta': [0.025, 0.03, 0.035],11              'silent': [1],12              'objective': ['binary:logistic'],13              'nthread': [16],14              'lambda': [3.5, 4, 4.5],15              'alpha': [0, 0.25], 16              'subsample': [0.85, 0.9, 9.5],17              'min_child_weight': [4.5, 5, 5.5],18              'booster': ['gbtree', 'dart'],19              'base_score': [0.0058], 20              'colsample_bytree': [0.5, 0.55, 0.6, 0.65]}21param_list = list(ParameterSampler(param_grid, 22    n_iter=100, random_state=285749))23#%% Load data of both train and test sets24# load numeric data25numeric = pd.read_hdf('../../data/data.hdf', 'numeric')26# labels for the train set27y_train = numeric.loc[('train', slice(None)), 'Response']28y_train = y_train.astype(np.int8)29# Import names of important features30important_features = pd.read_csv(31    '../benchmark_1/important_numeric_features.csv', 32    index_col=0, header=None)33important_features = list(important_features.values.ravel())34numeric = numeric[important_features]35numeric.index.names = ['set', 'Id']36#%% Load features: station flow37date_train = pd.read_csv(38    '../benchmark_2/train_station_flow.csv.gz', 39    index_col=0, header=None)40date_test = pd.read_csv(41    '../benchmark_2/test_station_flow.csv.gz', 42    index_col=0, header=None)43# Change index and column names44station_flow = pd.concat((date_train, date_test), keys=['train', 'test'])45station_flow.index.names = ['set', 'Id']46station_flow.columns = ['hash_station_flow0']47# Encode hash 48le = LabelEncoder()49station_flow['hash_station_flow0'] = le.fit_transform(station_flow)50# Join to numeric51numeric = numeric.join(station_flow)52del station_flow53gc.collect()54#%% Load features: benchmark 3, consective Id chunk55start_chunk = pd.read_csv('../benchmark_3/start_chunk.csv.gz', index_col=0)56# Group start chunks by train and test sets57start_chunk_train = start_chunk.loc[start_chunk.Response!=-1].drop(58    ['Response'], axis=1)59start_chunk_test = start_chunk.loc[start_chunk.Response==-1].drop(60    ['Response'], axis=1)61start_chunk = pd.concat((start_chunk_train, start_chunk_test), 62    keys=['train', 'test'])63start_chunk.index.names = ['set', 'Id']64# Join to numeric65numeric = numeric.join(start_chunk)66del start_chunk, start_chunk_test, start_chunk_train67gc.collect()68#%% Load features: benchmark 4, neighor time and response records69n = pd.read_csv('../benchmark_4/benchmark_4_neighbors.csv.gz', index_col=0)70# Group by train and test 71neighbor_train = n.loc[n.Response!=-1]72neighbor_train.drop(['Response'], axis=1, inplace=True)73neighbor_test = n.loc[n.Response==-1]74neighbor_test.drop(['Response'], axis=1, inplace=True)75neighbor = pd.concat((neighbor_train, neighbor_test), keys=['train', 'test'])76neighbor.index.names = ['set', 'Id']77# Join to numeric78numeric = numeric.join(neighbor)79del neighbor, neighbor_test, neighbor_train, n80gc.collect()81#%% Load features: benchmark 6, neighbor numeric features82numeric.sort_index(by=['Id'], inplace=True)83numeric = numeric.join(numeric[important_features].shift(), 84    rsuffix='_previous')85numeric = numeric.join(numeric[important_features].shift(-1),86    rsuffix='_next')87#%% Load features: benchmark 7, time features without MeanTimeDiff88time_features = pd.read_hdf('../benchmark_7/time_features_diff.hdf', 89    'time_features')90time_features.drop(['time_start', 'time_end', 'time_duration', 'Response'], 91    axis=1, inplace=True)92time_features.drop(time_features.columns[-40:], axis=1, inplace=True)93time_features.index.names = ['set', 'Id']94# Join to numeric95numeric = numeric.join(time_features)96del time_features97gc.collect()98#%% Save numeric data to a HDF for later use99for c in tqdm.tqdm(numeric.columns):100    if numeric[c].dtype==np.float64:101        numeric[c] = numeric[c].astype(np.float16)102numeric.to_hdf('numeric_b1_b7_nf149.hdf', 'numeric')...

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