130 lines
4.6 KiB
Python
130 lines
4.6 KiB
Python
import math
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import time
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import pandas as pd
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import scipy.stats as stats
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import matplotlib.pyplot as plt
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import matplotlib
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matplotlib.use('TkAgg')
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JAVA_INPUT = './data/try_Java_Jan_2022_last_revision.boa.output.txt'
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PYTHON_INPUT = './data/try_Python_Feb_2022_last_revision.boa.output.txt'
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SHOW_PLOTS = False
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SEED = 3
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def read_sample(file_path, title='', sample_size=0, show=True):
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# filename kann auch ein URL sein: "https://..../example.csv"
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df = pd.read_csv(file_path,
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sep=r"\[|\]\s=",
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engine="python",
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index_col=False,
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#nrows=25, # zum Testen nur kleine Anzahl einlesen
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skipinitialspace=True,
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names=['Variable', 'Project', 'Ratio'],
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usecols=['Project', 'Ratio']
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)
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if sample_size > 0:
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# feste seed zum besseren Vergleich
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df = df.sample(sample_size, random_state=int(SEED))
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if show:
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df.info()
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df.boxplot(column=['Ratio'], grid=False)
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df.hist(column=['Ratio'], grid=False)
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plt.title(title)
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plt.show()
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return df
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def cohen_d(x, y):
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# Mittelwerte der Gruppen
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mean_x, mean_y = x.mean(), y.mean()
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mean_diff = mean_x - mean_y
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# Varianzen der Gruppen
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var_x, var_y = x.var(ddof=1), y.var(ddof=1)
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# Stichprobengrößen der Gruppen
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size_x, size_y = len(x), len(y)
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# Gepoolte Varianz und Standardabweichung
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pool_var = ((size_x - 1) * var_x + (size_y - 1) * var_y) / (size_x + size_y - 2)
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pool_var = math.sqrt(pool_var)
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# Cohen's d
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d_val = mean_diff / pool_var
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return d_val
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def find_barrier(x_sample_path, y_sample_path, alpha=0.1, lower_limit=2, upper_limit=10000):
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left = lower_limit
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right = upper_limit
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barrier_size = -1
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# Binäre Suche nach der Schranke
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while left <= right:
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mid = (left + right) // 2
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x_sample = read_sample(x_sample_path, sample_size=mid, show=False)["Ratio"]
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y_sample = read_sample(y_sample_path, sample_size=mid, show=False)["Ratio"]
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stat, p_value = stats.mannwhitneyu(x_sample, y_sample, alternative="two-sided")
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if p_value >= alpha:
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# Vermerke die aktuelle untere Schranke
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barrier_size = mid
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# Kein signifikantes Ergebnis gefunden, probiere größere Probe
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left = mid + 1
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else:
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# Signifikantes Ergebnis gefunden, probiere kleinere Probe
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right = mid - 1
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x_sample = read_sample(x_sample_path, sample_size=barrier_size + 1, show=False)["Ratio"]
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y_sample = read_sample(y_sample_path, sample_size=barrier_size + 1, show=False)["Ratio"]
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stat, p_value = stats.mannwhitneyu(x_sample, y_sample, alternative="two-sided")
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print(f"\nSind die Unterschiede bei {barrier_size + 1} Proben signifikant? {str(p_value < alpha)}")
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x_sample = read_sample(x_sample_path, sample_size=barrier_size, show=False)["Ratio"]
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y_sample = read_sample(y_sample_path, sample_size=barrier_size, show=False)["Ratio"]
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stat, p_value = stats.mannwhitneyu(x_sample, y_sample, alternative="two-sided")
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print(f"Sind die Unterschiede bei {barrier_size} Proben signifikant? {str(p_value < alpha)}")
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return barrier_size
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def main():
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plt.close('all')
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print('Statistische Berechnungen zu Häufigkeiten (Übung 5)')
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print('\nEinlesen der ersten Stichprobe (Python)')
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python_sample = read_sample(PYTHON_INPUT, sample_size=1000, show=SHOW_PLOTS, title="Python")
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print('Mean:' + str(python_sample['Ratio'].mean()))
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print('Variance:' + str(python_sample['Ratio'].var()))
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print('\nEinlesen der zweiten Stichprobe (Java)')
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java_sample = read_sample(JAVA_INPUT, sample_size=1000, show=SHOW_PLOTS, title="Java")
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print('Mean:' + str(java_sample['Ratio'].mean()))
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print('Variance:' + str(java_sample['Ratio'].var()))
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print('\nStatistische Tests')
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# Aufgabenbearbeitung ab hier
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# Mann-Whitney-U-Test
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stat, p_value = stats.mannwhitneyu(python_sample['Ratio'], java_sample['Ratio'], alternative='two-sided')
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effect_size = cohen_d(python_sample['Ratio'], java_sample['Ratio'])
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print(f"Globaler Durchschnitt Python: {python_sample.get('Ratio').mean()}")
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print(f"Globaler Durchschnitt Java: {java_sample.get('Ratio').mean()}")
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print(f"Mann-Whitney-Test: Statistik {stat}, P-Wert {p_value}")
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print(f"Effektstärke (Cohen's d): {effect_size}")
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# Experimentelle Bestimmung der Schranke
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barrier = find_barrier(PYTHON_INPUT, JAVA_INPUT, alpha=0.01)
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print(f"Untere Schranke, ab welcher der Test nicht signifikant ist: {barrier}")
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# Press the green button in the gutter to run the script.
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if __name__ == '__main__':
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main()
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