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How to use filtered_strategy method in hypothesis

Best Python code snippet using hypothesis

draw_plots.py

Source:draw_plots.py

1#!/usr/bin/env python32import numpy as np3import matplotlib as mpl4import matplotlib.pyplot as plt5import scipy.stats6from math import sqrt7import operator8BASE_PATH = "data/"9PLOTS_PATH = "plots/"10DATA_INDEX = 211DEFAULT_INDEX = 312KNN_INDEX = 413LINEAR_INDEX = 514LOGISTIC_INDEX = 615TREE_INDEX = 716FOREST_INDEX = 817XMEANS_INDEX = 918DEFAULT_PARTITIONS_INDEX = 1119XMEANS_PARTITIONS_INDEX = 1720DELTA_PARTITIONS = DEFAULT_PARTITIONS_INDEX - DEFAULT_INDEX21def select_strategy(data):22    results = []23    for line in data:24        base = line[DATA_INDEX]25        best = None26        best_partitions = 027        besti = 028        for i in range(DEFAULT_PARTITIONS_INDEX,XMEANS_PARTITIONS_INDEX+1):29            current = line[i - DELTA_PARTITIONS]30            current_partitions = line[i]31            if current_partitions > 0:32                if not best or best > current:33                    best = current34                    best_partitions = current_partitions35                    besti = i36        results.append((base, best, besti - DELTA_PARTITIONS, line[0]))37    return results38def stats(filtered_strategy):39    a = [x[0] for x in filtered_strategy]40    b = [x[1] for x in filtered_strategy]41    ab = [x[0]-x[1] for x in filtered_strategy]42    print(scipy.stats.shapiro(a), np.mean(a), np.median(a), np.var(a))43    print(scipy.stats.shapiro(b), np.mean(b), np.median(b), np.var(b))44    print(scipy.stats.wilcoxon(ab))45    print(scipy.stats.norm.isf(5e-8 / 2))46def filter_partitions(data):47    return [x for x in data if sum(x[DEFAULT_PARTITIONS_INDEX:XMEANS_PARTITIONS_INDEX]) > 0]48def count_against_base(data, index=None):49    count = 050    totals = [0,0,0]51    for line in data:52        counts = [0,0,0]53        for idx in range(DEFAULT_INDEX,XMEANS_INDEX+1):54            if index and idx != index:55                continue56            if line[DATA_INDEX] < line[idx]:57                counts[0] = 158            elif line[DATA_INDEX] == line[idx]:59                counts[1] = 160            else:61                counts[2] = 162                break63        if counts[2] != 0:64            totals[2] += 165        elif counts[1] != 0:66            totals[1] += 167        else:68            totals[0] += 169    return totals70def do_line_plot(data):71    mpl.rcParams['figure.figsize'] = (11, 6)72    ab = sorted([(x[0]-x[1], x[-1]) for x in data], key=operator.itemgetter(0))73    closure_ab = ([(x+1,y) for x,(y,z) in enumerate(ab) if z == "Closure"], "o", "0.12", "Closure")74    lang_ab = ([(x+1,y) for x,(y,z) in enumerate(ab) if z == "Lang"], "o", "0.55", "Commons Lang")75    math_ab = ([(x+1,y) for x,(y,z) in enumerate(ab) if z == "Math"], "o", "0.95", "Commons Math")76    chart_ab = ([(x+1,y) for x,(y,z) in enumerate(ab) if z == "Chart"], "^", "0.12", "JFreeChart")77    time_ab = ([(x+1,y) for x,(y,z) in enumerate(ab) if z == "Time"], "^", "0.55", "JodaTime")78    mockito_ab = ([(x+1,y) for x,(y,z) in enumerate(ab) if z == "Mockito"], "^", "0.95", "Mockito")79    fig, ax = plt.subplots()80    ax.set_axisbelow(True)81    plt.grid(True, color='0.75')82    for cat, marker, c, label in [closure_ab, lang_ab, math_ab, chart_ab, time_ab, mockito_ab]:83        plt.scatter([x[0] for x in cat], [x[1] for x in cat], c=c, lw=0.5,s=50, marker=marker, edgecolor="0", label=label, zorder=2)84    ax.set_yscale('symlog')85    ax.set_ylim([-200,1000])86    ax.set_xlim([0,len(ab)+2])87    plt.ylabel("$\Delta{}C_d$")88    plt.xlabel("Subject Number")89    plt.legend(scatterpoints=1,90               loc='lower right',91               fontsize="10")92    ax.axvline(15.5, lw=1.5, color='red',ls='--', zorder=1)93    ax.axvline(77.5, lw=1.5, color='green',ls='--', zorder=1)94    ax.axvspan(0, 15.5, alpha=0.11, color='red', zorder=0)95    ax.axvspan(77.5, len(ab)+2, alpha=0.11, color='green', zorder=0)96    ax.annotate('$\Delta{}C_d \geq 0$', xy=(15.25, 300),  xycoords='data',97                xytext=(18, -3), textcoords='offset points',98                arrowprops=dict(arrowstyle="<-"), size=10,99                bbox=dict(boxstyle="square", pad=0, alpha=0)100                )101    ax.annotate('$\Delta{}C_d < 0$', xy=(15.5, 500),  xycoords='data',102                xytext=(-55, -3), textcoords='offset points',103                arrowprops=dict(arrowstyle="<-"), size=10,104                bbox=dict(boxstyle="square", pad=0, alpha=0)105                )106    ax.annotate('$\Delta{}C_d > 0$', xy=(77.25, 300),  xycoords='data',107                xytext=(18, -3), textcoords='offset points',108                arrowprops=dict(arrowstyle="<-"), size=10,109                bbox=dict(boxstyle="square", pad=0, alpha=0)110                )111    ax.annotate('$\Delta{}C_d \leq 0$', xy=(77.5, 500),  xycoords='data',112                xytext=(-55, -3), textcoords='offset points',113                arrowprops=dict(arrowstyle="<-"), size=10,114                bbox=dict(boxstyle="square", pad=0, alpha=0)115                )116    plt.tight_layout()117    plt.savefig(PLOTS_PATH + "delta-cd.pdf", bbox_inches="tight")118def do_plot(results, my_xticks, filename, xlabel='', ylabel='', title=''):119    # data to plot120    n_groups = len(results)121    #results = count_against_base(data)122    results_better = [x[0] for x in results]123    results_same = [x[1] for x in results]124    results_worse = [x[2] for x in results]125    # create plot126    fig, ax = plt.subplots()127    ax.set_axisbelow(True)128    ax.yaxis.grid(True, color="0.85")129    index = np.arange(n_groups)130    bar_width = 0.275131    opacity = 0.8132    rects1 = plt.bar(index, results_better, bar_width,133                     color='0.35', lw=1, edgecolor="0",134                     label='$\Delta{}C_d$ < 0')135    rects2 = plt.bar(index + bar_width, results_same, bar_width,136                     color='0.65', lw=1, edgecolor="0",137                     label='$\Delta{}C_d$ = 0')138    rects3 = plt.bar(index + bar_width + bar_width, results_worse, bar_width,139                     color='0.95', lw=1, edgecolor="0",140                     label='$\Delta{}C_d$ > 0')141    plt.xlabel(xlabel)142    plt.ylabel(ylabel)143    plt.xticks(index + 1.5*bar_width, my_xticks)144    plt.legend(fontsize="12")145    plt.tight_layout()146    plt.savefig(PLOTS_PATH + filename, bbox_inches="tight")147if __name__ == "__main__":148    data = []149    for f in ["Closure.csv", "Lang.csv", "Math.csv", "Chart.csv","Time.csv","Mockito.csv"]:150        with open(BASE_PATH + f) as projectfile:151            for index, line in enumerate(projectfile):152                if index==0:153                    continue154                line = line.rstrip().split(",")155                for x in range(2,len(line)):156                    line[x] = float(line[x])157                data.append(line)158    data = filter_partitions(data)159    fstrat = select_strategy(data)160    #stats(fstrat)161    results = [count_against_base(data, DEFAULT_INDEX),162               count_against_base(data, XMEANS_INDEX),163               count_against_base(data, KNN_INDEX),164               count_against_base(data, LINEAR_INDEX),165               count_against_base(data, LOGISTIC_INDEX),166               count_against_base(data, TREE_INDEX),167               count_against_base(data, FOREST_INDEX)168               ]169    do_plot(results, ['Sign','X-means', 'k-NN','Linear', 'Logistic', 'Tree', 'Forest'],170            'per-strategy.pdf', xlabel='Strategies',ylabel='# Subjects',171            title='Effort vs Base: All Projects')...

gc_spyu.py

Source:gc_spyu.py

1#!/usr/bin/env python32from gc_spyu import spyu3from gc_spyu import utils4from gc_spyu.view.on_run_conclusion import on_run_conclusion5import debug6import pprint7if __name__ == "__main__":8    debug.dlog("starting")9    spyu_ctx = spyu.spyuCTX()10    utils.run_golem_example(spyu_ctx())11    mySummaryLogger, nodeInfoIds, myModel = spyu_ctx.get_results()12    merged_list = mySummaryLogger.providersFailed13    merged_list.extend(mySummaryLogger.skipped)14    debug.dlog(f"merged_list: {merged_list}")15    if len(merged_list) > 0:16        msg = (17            "The following providers were skipped because they were"18            " unreachable or otherwise uncooperative:"19        )20        print(f"\n{msg}")21        print("-" * (len(msg) - 1))22        merged_pairs = set()23        for dict_ in merged_list:24            merged_pairs.add(25                (26                    dict_["name"],27                    dict_["address"],28                )29            )30        for name, address in merged_pairs:31            print(f"{name}@{address}")32        print("-" * (len(msg) - 1))33    if len(spyu_ctx.filtered_strategy.lowscored) > 0:34        msg = (35            "The following providers were skipped because they failed"36            " the scoring criteria:"37        )38        print(f"\n{msg}")39        print("-" * (len(msg) - 1))40        for low in spyu_ctx.filtered_strategy.lowscored:41            print(f"{low}")42        print("-" * (len(msg) - 1))43    if len(spyu_ctx.filtered_strategy.whitelist) > 0:44        msg = (45            "The following providers were skipped because they were not"46            " seen on the network:"47        )48        print(f"\n{msg}")49        print("-" * (len(msg) - 1))50        for unseen in spyu_ctx.filtered_strategy.whitelist:51            print(f"{unseen}")52        print("-" * (len(msg) - 1))53    print(54        "\n\033[0;32mTotal glm spent from all engine runs: \033[1m"55        + str(mySummaryLogger.sum_invoices())56        + "\033[0m"57    )58    if isinstance(nodeInfoIds, list) and (59        len(nodeInfoIds) > 0 or len(spyu_ctx.filtered_strategy.dupIds) > 060    ):61        try:62            on_run_conclusion(63                mySummaryLogger, nodeInfoIds, myModel, spyu_ctx.filtered_strategy.dupIds64            )65        except KeyboardInterrupt:...

Strategy.py

Source:Strategy.py

1import numpy as np2from numpy import ndarray3class Strategy:4    @classmethod5    def random(cls, size: int):6        return np.ones(size)7    @classmethod8    def normalize(cls, strategy: ndarray, _filter: ndarray = None, frequencies: ndarray = None):9        if _filter is None:10            _filter = np.ones(len(strategy))11        filtered_strategy = strategy * _filter12        normalizing_sum = sum(filtered_strategy)13        if normalizing_sum == 0:14            return np.zeros(len(strategy))15        return filtered_strategy / normalizing_sum16    @classmethod17    def from_utility(cls, strategy: ndarray, utilities: ndarray):18        return np.array([s_val if r_val <= 019                        else r_val if s_val == 120                        else (r_val + s_val) / 221                        for idx, (s_val, r_val) in enumerate(zip(strategy, utilities))])22    # @classmethod23    # def from_regrets(cls, strategy: ndarray, regrets: ndarray):24    #     return np.array([s_val if r_val <= 025    #                     else r_val if s_val == 126    #                     else (r_val + s_val) / 2...

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