removed old file"

multiview_platform/mono_multi_view_classifiers/monoview/export_results.py

-#!/usr/bin/env python
-
-""" Library: Functions to export preds to CSV or plots """
-
-# Import built-in modules
-import os  # for iteration throug directories
-import string  # to generate a range of letters
-
-import matplotlib.pyplot as plt  # for Plots
-import numpy as np  # for Numpy Arrays
-# Import 3rd party modules
-import pandas as pd  # for Series and DataFrames
-# matplotlib.use('Agg')
-from matplotlib.offsetbox import AnchoredOffsetbox, TextArea, \
-    HPacker  # to generate the Annotations in plot
-from pylab import rcParams  # to change size of plot
-from scipy.interpolate import interp1d  # to Interpolate data
-from sklearn import metrics  # For stastics on classification
-
-# Import own modules
-
-# Author-Info
-__author__ = "Nikolas Huelsmann"
-__status__ = "Prototype"  # Production, Development, Prototype
-__date__ = 2016_03_25
-
-
-#### Export Features to CSV
-def exportPandasToCSV(pandasSorDF, directory, filename):
-    file = directory + filename
-
-    # Makes sure that the file does not yet exist
-    if os.path.isfile(file + ".csv"):
-        for i in range(1, 20):
-            testFileName = filename + "-" + str(i) + ".csv"
-            if not os.path.isfile(directory + testFileName):
-                pandasSorDF.to_csv(directory + testFileName, sep=';')
-                break
-
-    else:
-        pandasSorDF.to_csv(file + ".csv", sep=';')
-
-
-def exportNumpyToCSV(numpyArray, directory, filename, format):
-    file = directory + filename
-
-    # Makes sure that the file does not yet exist
-    if os.path.isfile(file + ".csv"):
-        for i in range(1, 20):
-            testFileName = filename + "-" + str(i) + ".csv"
-            if not os.path.isfile(directory + testFileName):
-                np.savetxt(directory + testFileName, numpyArray, delimiter=";",
-                           fmt=format)
-                break
-
-    else:
-        np.savetxt(file + ".csv", numpyArray, delimiter=";", fmt=format)
-
-
-#### Rendering of results
-
-### Rendering of Score and Time
-def showScoreTime(directory, filename, store, resScore, resTime, rangeX,
-                  parameter, feat_desc, cl_desc, fig_desc,
-                  y_desc1,
-                  y_desc2):
-    # Determine interpolated functions
-    f_score_interp = interp1d(rangeX, resScore, kind='quadratic')
-    f_time_interp = interp1d(rangeX, resTime, kind='quadratic')
-
-    # Change size of plot
-    rcParams['figure.figsize'] = 20, 10
-
-    # Figure1 with subplot
-    fig, ax1 = plt.subplots()
-
-    # plt.plot(x, y, type of line)
-    # Generating X-Axis
-    xnew = np.linspace(0, max(rangeX), num=100, endpoint=True)
-
-    # First Axis for Score (left)
-    ax1.plot(rangeX, resScore, 'bo', rangeX, f_score_interp(rangeX), 'b-')
-    ax1.set_xlabel(parameter, fontsize=16)
-    ax1.set_ylabel(y_desc1, color='b', fontsize=16)
-    for tl in ax1.get_yticklabels():
-        tl.set_color('b')
-
-    # First Axis for Time (right)
-    ax2 = ax1.twinx()
-    ax2.plot(rangeX, resTime, 'ro', rangeX, f_time_interp(rangeX), 'r-')
-    ax2.set_ylabel(y_desc2, color='r', fontsize=16)
-    for tl in ax2.get_yticklabels():
-        tl.set_color('r')
-
-    letters = string.lowercase[0:len(rangeX)]
-    legend = ""
-    for act_x, act_score, act_time, act_feat_desc, letter, act_cl_desc in zip(
-            rangeX, resScore, resTime, feat_desc,
-            letters, cl_desc):
-        # Add a letter (a,b,c,..) to each DataPoint
-        ax1.annotate(letter, xy=(act_x, act_score), xytext=(act_x, act_score))
-        ax2.annotate(letter, xy=(act_x, act_time), xytext=(act_x, act_time))
-        # Creates a legend with description of feature and classificator of each datapoint
-        legend = legend + letter + ") Feature: " + act_feat_desc + "; Classifier: " + act_cl_desc + "\n"
-
-    # Remove last \n
-    legend = legend[:-1]
-
-    box1 = TextArea(legend, textprops=dict(color="k"))
-    box = HPacker(children=[box1],
-                  align="center",
-                  pad=0, sep=5)
-
-    anchored_box = AnchoredOffsetbox(loc=3,
-                                     child=box, pad=0.2,
-                                     frameon=True,
-                                     bbox_to_anchor=(0, 1.04),
-                                     # to change the place of the legend (text above of figure)
-                                     bbox_transform=ax1.transAxes,
-                                     borderpad=1.0,
-                                     )
-    ax1.add_artist(anchored_box)
-    fig.subplots_adjust(top=0.7)
-
-    ax1.legend(['Score data', 'Score Interpolated'], loc='upper left')
-    ax2.legend(['Time data', 'Time Interpolated'], loc='lower right')
-
-    plt.title(fig_desc, fontsize=18)
-
-    if store:
-        # Makes sure that the file does not yet exist
-        file = directory + filename
-
-        if os.path.isfile(file + ".png"):
-            for i in range(1, 20):
-                testFileName = filename + "-" + str(i) + ".png"
-                if not os.path.isfile(directory + testFileName):
-                    plt.savefig(directory + testFileName, transparent=True)
-                    break
-
-        else:
-            plt.savefig(file, transparent=True)
-    else:
-        plt.show()
-
-    plt.close()
-
-
-### Result comparision per class
-def calcScorePerClass(np_labels, np_output):
-    pd_label_test = pd.Series(np_labels)
-    pd_output = pd.Series(np_output)
-    score = []
-
-    for i in pd_label_test.unique():
-        matches = sum(pd_label_test[pd_label_test == i] == pd_output[
-            pd_label_test[pd_label_test == i].index])
-        count = float(len(pd_label_test[pd_label_test == i]))
-        score.append(matches / count)
-
-    score = np.array(score)
-    return score
-
-
-### Bar-Plot for score
-
-def showResults(directory, filename, db, feat, score):
-    plt.bar(range(0, len(score)), score * 100, 1)
-    plt.xlabel('ClassLabels')
-    plt.ylabel('Precision in %')
-    plt.title(
-        'results of ' + feat + '-Classification\n for ' + db + ' Database')
-    plt.axis([0, len(score), 0, 100])
-    plt.xticks(range(0, len(score), 5))
-
-    # Makes sure that the file does not yet exist
-    file = directory + filename
-
-    if os.path.isfile(file + ".png"):
-        for i in range(1, 20):
-            testFileName = filename + "-" + str(i) + ".png"
-            if not os.path.isfile(directory + testFileName):
-                plt.savefig(directory + testFileName, transparent=True)
-                break
-
-    else:
-        plt.savefig(file, transparent=True)
-
-    plt.close()
-
-    # instead of saving - decomment plt.show()
-    # plt.show()
-
-
-# Function to calculate the accuracy score for test data
-def accuracy_score(y_test, y_test_pred):
-    return metrics.accuracy_score(y_test, y_test_pred)
-
-
-# Function to calculate a report of classifiaction and store it
-def classification_report_df(directory, filename, y_test, y_test_pred, labels,
-                             target_names):
-    # Calculate the metrics
-    precision, recall, f1score, support = metrics.precision_recall_fscore_support(
-        y_test, y_test_pred, beta=1.0,
-        labels=labels, pos_label=None,
-        average=None)
-
-    # turn result into DataFrame
-    scores_df = pd.DataFrame(data=[precision, recall, f1score, support])
-    scores_df.index = ["Precision", "Recall", "F1", "Support"]
-    scores_df.columns = target_names
-    scores_df = scores_df.transpose()
-
-    # Store result as CSV
-    exportPandasToCSV(scores_df, directory, filename)
-
-    # return the results
-    return scores_df
-
-
-# Function to calculate a report of classifiaction and store it
-def confusion_matrix_df(directory, filename, y_test, y_test_pred, target_names):
-    # Transform into pd Series
-    y_actu = pd.Series(y_test, name='Actual')
-    y_pred = pd.Series(y_test_pred, name='Predicted')
-
-    # Calculate confusion matrix
-    df_confusion = pd.crosstab(y_actu, y_pred, rownames=['Actual'],
-                               colnames=['Predicted'], margins=True)
-
-    # Normalization of confusion matrix
-    df_conf_norm = df_confusion / df_confusion.sum(axis=1)
-    df_conf_norm.index = target_names + ['All']
-    df_conf_norm.columns = target_names + ['All']
-
-    # Add Row: Actual / Column: Predicted into first cell [0,0]
-
-    # Store result as CSV
-    exportPandasToCSV(df_conf_norm, directory, filename)
-
-    return df_conf_norm
-
-
-def plot_confusion_matrix(directory, filename, df_confusion,
-                          title='Confusion matrix', cmap=plt.cm.gray_r):
-    plt.matshow(df_confusion, cmap=cmap)  # imshow
-    # plt.title(title)
-    plt.colorbar()
-    tick_marks = np.arange(len(df_confusion.columns))
-    plt.xticks(tick_marks, df_confusion.columns, rotation=45)
-    plt.yticks(tick_marks, df_confusion.index)
-    # plt.tight_layout()
-    plt.ylabel(df_confusion.index.name)
-    plt.xlabel(df_confusion.columns.name)
-
-    # Makes sure that the file does not yet exist
-
-    file = directory + filename
-
-    if os.path.isfile(file + ".png"):
-        for i in range(1, 20):
-            testFileName = filename + "-" + str(i) + ".png"
-            if not os.path.isfile(directory + testFileName):
-                plt.savefig(directory + testFileName, transparent=True)
-                break
-
-    else:
-        plt.savefig(file, transparent=True)
-
-    plt.close()
-
-
-def classification_stats(directory, filename, scores_df, acc):
-    # Accuracy on test over all classes
-    acc = acc
-
-    # Top 10 classes by F1-Score
-    top10 = scores_df.sort_values(["F1"], ascending=False).head(10)
-    top10 = list(top10.index)
-
-    # Worst 10 classes by F1-Score
-    worst10 = scores_df.sort_values(["F1"], ascending=True).head(10)
-    worst10 = list(worst10.index)
-
-    # Ratio of classes with F1-Score==0 of all classes
-    ratio_zero = float(
-        float(len(scores_df[scores_df.F1 == 0])) / float(len(scores_df)))
-
-    # Mean of F1-Score of top 10 classes by F1-Score
-    mean_10 = np.mean(
-        scores_df.sort_values(["F1"], ascending=False).head(10).F1)
-
-    # Mean of F1-Score of top 20 classes by F1-Score
-    mean_20 = np.mean(
-        scores_df.sort_values(["F1"], ascending=False).head(20).F1)
-
-    # Mean of F1-Score of top 30 classes by F1-Score
-    mean_30 = np.mean(
-        scores_df.sort_values(["F1"], ascending=False).head(30).F1)
-
-    # Create DataFrame with stats
-    d = {'Statistic': ['Accuracy score on test', 'Top 10 classes by F1-Score',
-                       'Worst 10 classes by F1-Score',
-                       'Ratio of classes with F1-Score==0 of all classes',
-                       'Mean of F1-Score of top 10 classes by F1-Score',
-                       'Mean of F1-Score of top 20 classes by F1-Score',
-                       'Mean of F1-Score of top 30 classes by F1-Score'],
-         'Values': [acc, top10, worst10, ratio_zero, mean_10, mean_20, mean_30]}
-    df_stats = pd.DataFrame(d)
-
-    # Store result as CSV
-    exportPandasToCSV(df_stats, directory, filename)
-
-    # return pandas
-    return df_stats