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How to use from_results method in Lemoncheesecake

Best Python code snippet using lemoncheesecake

farm_layout.py

Source:farm_layout.py

1import json2import numpy as np3import pandas as pd4import matplotlib.pyplot as plt5import foxes.constants as FC6import foxes.variables as FV7from foxes.output.output import Output8class FarmLayoutOutput(Output):9    """10    Plot the farm layout11    Parameters12    ----------13    farm : foxes.WindFarm14        The wind farm15    farm_results : xarray.Dataset, optional16        The wind farm calculation results17    from_results : bool, optional18        Flag for coordinates from results data19    results_state : int, optional20        The state index, for from_res21    D : float, optional22        The rotor diameter, if not from data23    Attributes24    ----------25    farm : foxes.WindFarm26        The wind farm27    fres : xarray.Dataset28        The wind farm calculation results29    from_res : bool30        Flag for coordinates from results data31    results_state : int32        The state index, for from_res33    D : float34        The rotor diameter, if not from data35    """36    def __init__(37        self, farm, farm_results=None, from_results=False, results_state=None, D=None38    ):39        self.farm = farm40        self.fres = farm_results41        self.from_res = from_results42        self.rstate = results_state43        self.D = D44        if from_results and farm_results is None:45            raise ValueError(f"Missing farm_results for switch from_results.")46        if from_results and results_state is None:47            raise ValueError(f"Please specify results_state for switch from_results.")48    def get_layout_data(self):49        """50        Returns wind farm layout.51        Returns52        -------53        numpy.ndarray :54            The wind farm layout, shape:55            (n_turbines, 3) where the 356            represents x, y, h57        """58        data = np.zeros([self.farm.n_turbines, 3], dtype=FC.DTYPE)59        if self.from_res:60            data[:, 0] = self.fres[FV.X][self.rstate]61            data[:, 1] = self.fres[FV.Y][self.rstate]62            data[:, 2] = self.fres[FV.H][self.rstate]63        else:64            for ti, t in enumerate(self.farm.turbines):65                data[ti, :2] = t.xy66                data[ti, 2] = t.H67        return data68    def get_layout_dict(self):69        """70        Returns wind farm layout.71        Returns72        -------73        dict :74            The wind farm layout in dict75            format, as in json output76        """77        data = self.get_layout_data()78        out = {self.farm.name: {}}79        for ti, p in enumerate(data):80            t = self.farm.turbines[ti]81            out[self.farm.name][t.name] = {82                "id": t.index,83                "name": t.name,84                "UTMX": p[0],85                "UTMY": p[1],86            }87        return out88    def get_figure(89        self,90        fontsize=8,91        figsize=None,92        annotate=1,93        title=None,94        fig=None,95        ax=None,96        normalize_D=False,97        ret_im=False,98        **kwargs,99    ):100        """101        Creates farm layout figure.102        Parameters103        ----------104        fontsize : int, optional105            Size of the turbine numbers106        figsize : tuple, optional107            The figsize for plt.Figure108        annotate : int, optional109            Turbine index printing, Choices:110            0 = No annotation111            1 = Turbine indices112            2 = Turbine names113        title : str, optional114            The plot title, or None for automatic115        fig : matplotlib.pyplot.Figure, optional116            The figure object to which to add117        ax : matplotlib.pyplot.Axis, optional118            The axis object, to which to add119        normalize_D : bool120            Normalize x, y wrt rotor diameter121        ret_im : bool122            Flag for returned image object123        **kwargs : dict, optional124            Parameters forwarded to `matplotlib.pyplot.scatter`125        Returns126        -------127        ax : matplotlib.pyplot.Axis128            The axis object129        im : matplotlib.pyplot.PathCollection, optional130            The image object131        """132        if fig is None:133            fig = plt.figure(figsize=figsize)134            ax = fig.add_subplot(111)135        else:136            ax = fig.axes[0] if ax is None else ax137        # for b in self.farm.boundary_geometry:138        #    b.add_to_figure(fig, ax)139        D = self.D140        if normalize_D and D is None:141            if self.from_res:142                if self.fres[FV.D].min() != self.fres[FV.D].max():143                    raise ValueError(f"Expecting uniform D, found {self.fres[FV.D]}")144                D = self.fres[FV.D][0]145            else:146                D = None147                for ti, t in enumerate(self.farm.turbines):148                    hD = t.D149                    if D is None:150                        D = hD151                    elif D != hD:152                        raise ValueError(153                            f"Turbine {ti} has wrong rotor diameter, expecting D = {D} m, found D = {hD} m"154                        )155                if D is None:156                    raise ValueError(157                        f"Variable '{FV.D}' not found in turbines. Maybe set explicitely, or try from_results?"158                    )159        data = self.get_layout_data()160        x = data[:, 0] / D if normalize_D else data[:, 0]161        y = data[:, 1] / D if normalize_D else data[:, 1]162        n = range(len(x))163        kw = {"c": "orange"}164        kw.update(**kwargs)165        im = ax.scatter(x, y, **kw)166        if annotate == 1:167            for i, txt in enumerate(n):168                ax.annotate(int(txt), (x[i], y[i]), size=fontsize)169        elif annotate == 2:170            for i, t in enumerate(self.farm.turbines):171                ax.annotate(t.name, (x[i], y[i]), size=fontsize)172        ti = (173            title174            if title is not None175            else self.farm.name176            if D is None or not normalize_D177            else f"{self.farm.name} (D = {D} m)"178        )179        ax.set_title(ti)180        ax.set_xlabel("x [m]" if not normalize_D else "x [D]")181        ax.set_ylabel("y [m]" if not normalize_D else "y [D]")182        ax.grid()183        # if len(self.farm.boundary_geometry) \184        #    or ( min(x) != max(x) and min(y) != max(y) ):185        if min(x) != max(x) and min(y) != max(y):186            ax.set_aspect("equal", adjustable="box")187        ax.autoscale_view(tight=True)188        if ret_im:189            return ax, im190        return ax191    def write_plot(self, file_path=None, fontsize=8, **kwargs):192        """193        Writes the layout plot to file.194        The kwargs are forwarded to self.get_figure195        Parameters196        ----------197        file_path : str198            The file into which to plot, or None199            for default200        fontsize : int201            Size of the turbine numbers202        """203        ax = self.get_figure(fontsize=fontsize, ret_im=False, **kwargs)204        fig = ax.get_figure()205        fname = file_path if file_path is not None else self.farm.name + ".png"206        fig.savefig(fname, bbox_inches="tight")207        plt.close(fig)208    def write_xyh(self, file_path=None):209        """210        Writes xyh layout file.211        Parameters212        ----------213        file_path : str214            The file into which to plot, or None215            for default216        """217        data = self.get_layout_data()218        fname = file_path if file_path is not None else self.farm.name + ".xyh"219        np.savetxt(fname, data, header="x y h")220    def write_csv(self, file_path=None):221        """222        Writes csv layout file.223        Parameters224        ----------225        file_path : str226            The file into which to plot, or None227            for default228        """229        data = self.get_layout_data()230        fname = file_path if file_path is not None else self.farm.name + ".csv"231        lyt = pd.DataFrame(232            index=range(len(data)), columns=["id", "name", "x", "y", "h", "D"]233        )234        lyt.index.name = "index"235        lyt["id"] = [t.info["id"] for t in self.farm.turbines]236        lyt["name"] = [t.info["name"] for t in self.farm.turbines]237        lyt["x"] = data[:, 0]238        lyt["y"] = data[:, 1]239        lyt["h"] = data[:, 2]240        lyt["D"] = [t.D for t in self.farm.turbines]241        lyt.to_csv(fname)242    def write_json(self, file_path=None):243        """244        Writes xyh layout file.245        Parameters246        ----------247        file_path : str248            The file into which to plot, or None249            for default250        """251        data = self.get_layout_dict()252        fname = file_path if file_path is not None else self.farm.name + ".json"253        with open(fname, "w") as outfile:...

test_performance_curves.py

Source:test_performance_curves.py

...47        np.testing.assert_almost_equal(curves.tpr(), tp / 9)48        np.testing.assert_almost_equal(curves.fpr(), (10 - tn) / 10)49    @patch("Orange.evaluation.performance_curves.Curves.__init__",50           return_value=None)51    def test_curves_from_results(self, init):52        res = Results()53        ytrue, probs = self.data.T54        res.actual = ytrue.astype(float)55        res.probabilities = np.vstack((1 - probs, probs)).T.reshape(1, -1, 2)56        Curves.from_results(res)57        cytrue, cprobs = init.call_args[0]58        np.testing.assert_equal(cytrue, ytrue)59        np.testing.assert_equal(cprobs, probs)60        Curves.from_results(res, target_class=0)61        cytrue, cprobs = init.call_args[0]62        np.testing.assert_equal(cytrue, 1 - ytrue)63        np.testing.assert_equal(cprobs, 1 - probs)64        res.actual = ytrue.astype(float)65        res.probabilities = np.random.random((2, 19, 2))66        res.probabilities[1] = np.vstack((1 - probs, probs)).T67        Curves.from_results(res, model_index=1)68        cytrue, cprobs = init.call_args[0]69        np.testing.assert_equal(cytrue, ytrue)70        np.testing.assert_equal(cprobs, probs)71        self.assertRaises(ValueError, Curves.from_results, res)72        ytrue[ytrue == 0] = 2 * (np.arange(10) % 2)73        res.actual = ytrue.astype(float)74        res.probabilities = np.random.random((2, 19, 3))75        res.probabilities[1] = np.vstack(76            ((1 - probs) / 3, probs, (1 - probs) * 2 / 3)).T77        Curves.from_results(res, model_index=1, target_class=1)78        cytrue, cprobs = init.call_args[0]79        np.testing.assert_equal(cytrue, ytrue == 1)80        np.testing.assert_equal(cprobs, probs)81        Curves.from_results(res, model_index=1, target_class=0)82        cytrue, cprobs = init.call_args[0]83        np.testing.assert_equal(cytrue, ytrue == 0)84        np.testing.assert_equal(cprobs, (1 - probs) / 3)85        Curves.from_results(res, model_index=1, target_class=2)86        cytrue, cprobs = init.call_args[0]87        np.testing.assert_equal(cytrue, ytrue == 2)88        np.testing.assert_equal(cprobs, (1 - probs) * 2 / 3)89        self.assertRaises(ValueError, Curves.from_results, res, model_index=1)90    @patch("Orange.evaluation.performance_curves.Curves.__init__",91           return_value=None)92    def test_curves_from_results_nans(self, init):93        res = Results()94        ytrue, probs = self.data.T95        ytrue[0] = np.nan96        probs[-1] = np.nan97        res.actual = ytrue.astype(float)98        res.probabilities = np.vstack((1 - probs, probs)).T.reshape(1, -1, 2)99        Curves.from_results(res)100        cytrue, cprobs = init.call_args[0]101        np.testing.assert_equal(cytrue, ytrue[1:-1])...

generate_truncated_json

Source:generate_truncated_json

1#!/usr/bin/python2.72"""3Given a full set of results, generate new JSON files with truncated iterations.4MUST be run before plot_truncated_iterations.5"""6import argparse7import os.path8import sys9sys.path.insert(0, os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "warmup_stats"))10from warmup.krun_results import read_krun_results_file, write_krun_results_file11MAX_ITERS = 199012MIN_ITERS = 1013ITER_STEP = -1014WINDOW_PROPORTION = 0.1  # Needed for outlier script options.15STEADY_PROPORTION = 0.25  # Needed for changepoint script options.16_BLANK_BENCHMARK = { 'wallclock_times': dict(), # Measurement data.17                    'core_cycle_counts': dict(), 'aperf_counts': dict(),18                    'mperf_counts': dict(), 'audit': dict(), 'config': '',19                    'reboots': 0, 'starting_temperatures': list(),20                    'eta_estimates': list(), 'error_flag': list(),21                    'all_outliers': dict(), 'unique_outliers': dict(),22                    'common_outliers': dict(), }23def parse_krun_file_with_changepoints(json_files):24    """Simplified version of the same function from krun_results."""25    data_dictionary = dict()26    window_size = None27    for filename in json_files:28        assert os.path.exists(filename), 'File %s does not exist.' % filename29        data = read_krun_results_file(filename)30        machine_name = data['audit']['uname'].split(' ')[1]31        if '.' in machine_name:  # Remove domain, if there is one.32            machine_name = machine_name.split('.')[0]33        if machine_name not in data_dictionary:34            data_dictionary[machine_name] = data35        else:36            assert 'Found two datasets from machine %s (expected one)' % machine_name37        if window_size is None:38            window_size = data['window_size']39        else:40            assert window_size == data['window_size'], \41                   ('Cannot summarise categories generated with different window-size '42                    'options. Please re-run the mark_outliers_in_json script.')43    return data['audit'], window_size, data_dictionary44def copy_results(last_iter, audit, window_size, from_results, to_results):45    """Copy results into a new dict. ASSUME only one machine per results file."""46    to_results['audit'] = audit47    to_results['window_size'] = window_size48    for mc in from_results:49        for key in from_results[mc]['wallclock_times']:50            to_results['wallclock_times'][key] = list()51            for p_exec in xrange(len(from_results[mc]['wallclock_times'][key])):52                to_results['wallclock_times'][key].append(from_results[mc]['wallclock_times'][key][p_exec][:last_iter])53def create_outlier_filename(in_file_name, window):54    directory = os.path.dirname(in_file_name)55    basename = os.path.basename(in_file_name)56    if basename.endswith('.json.bz2'):57        root_name = basename[:-9]58    else:59        root_name = os.path.splitext(basename)[0]60    base_out = ('%s_outliers_w%d.json.bz2' % (root_name, window))61    return os.path.join(directory, base_out)62def generate_truncated_json(json_file):63    """Generate an output file for each truncated data set."""64    warmup_stats_bin_dir = os.path.join(os.path.dirname(os.path.dirname(os.path.abspath(__file__))), "warmup_stats", "bin")65    outlier_script = os.path.join(warmup_stats_bin_dir, 'mark_outliers_in_json')66    cpt_script = os.path.join(warmup_stats_bin_dir, 'mark_changepoints_in_json')67    directory = os.path.dirname(json_file)68    audit, window_size, original_json = parse_krun_file_with_changepoints([json_file])69    for last_iter in range(MAX_ITERS, MIN_ITERS, ITER_STEP):70        print 'Truncated to iteration:', last_iter71        new_json_data = _BLANK_BENCHMARK72        copy_results(last_iter, audit, window_size, original_json, new_json_data)73        outfile = os.path.join(directory, ('truncated_%g.json.bz2') % last_iter)74        write_krun_results_file(new_json_data, outfile)75        window = int(last_iter * WINDOW_PROPORTION)76        os.system('%s %s --window %d' % (outlier_script, outfile, window))77        steady = int(last_iter * STEADY_PROPORTION)78        os.system('%s %s --steady %d' % (cpt_script, create_outlier_filename(outfile, window), steady))79def create_cli_parser():80    """Create a parser to deal with command line switches."""81    script = os.path.basename(__file__)82    description = (('Generate results files by repeatedly truncating a Krun data file.\n' +83                    '\n\nExample usage:\n\n' +84                    '\t$ python %s results.json.bz2') % script)85    parser = argparse.ArgumentParser(description=description,86                                     formatter_class=argparse.RawTextHelpFormatter)87    parser.add_argument('results_file', action='store', default='.', type=str,88                        help='Results file to be truncated.')89    return parser90if __name__ == '__main__':91    parser = create_cli_parser()92    options = parser.parse_args()...

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