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Commit d0299a1d authored by Sören Henning's avatar Sören Henning
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Fix output path

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    analysis/scalability-graph.ipynb
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    %% Cell type:code id: tags:
    ```
    print("hello")
    ```
    %% Cell type:code id: tags:
    ```
    import os
    from datetime import datetime, timedelta, timezone
    import pandas as pd
    from sklearn.linear_model import LinearRegression
    import matplotlib.pyplot as plt
    ```
    %% Cell type:code id: tags:
    ```
    os.getcwd()
    ```
    %% Cell type:code id: tags:
    ```
    exp_id = 2012
    warmup_sec = 60
    warmup_partitions_sec = 120
    threshold = 2000 #slope
    #directory = '../results'
    directory = '<path-to>/results'
    directory_out = '<path-to>/results-inst'
    ```
    %% Cell type:code id: tags:outputPrepend,outputPrepend
    ```
    #exp_id = 35
    #os.chdir("./results-final")
    raw_runs = []
    filenames = [filename for filename in os.listdir(directory) if filename.startswith(f"exp{exp_id}") and filename.endswith("totallag.csv")]
    for filename in filenames:
    #print(filename)
    run_params = filename[:-4].split("_")
    dim_value = run_params[2]
    instances = run_params[3]
    df = pd.read_csv(os.path.join(directory, filename))
    #input = df.loc[df['topic'] == "input"]
    input = df
    #print(input)
    input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']
    #print(input)
    #print(input.iloc[0, 'timestamp'])
    regress = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up
    #regress = input
    #input.plot(kind='line',x='timestamp',y='value',color='red')
    #plt.show()
    X = regress.iloc[:, 2].values.reshape(-1, 1) # values converts it into a numpy array
    Y = regress.iloc[:, 3].values.reshape(-1, 1) # -1 means that calculate the dimension of rows, but have 1 column
    linear_regressor = LinearRegression() # create object for the class
    linear_regressor.fit(X, Y) # perform linear regression
    Y_pred = linear_regressor.predict(X) # make predictions
    trend_slope = linear_regressor.coef_[0][0]
    #print(linear_regressor.coef_)
    row = {'dim_value': int(dim_value), 'instances': int(instances), 'trend_slope': trend_slope}
    #print(row)
    raw_runs.append(row)
    lags = pd.DataFrame(raw_runs)
    ```
    %% Cell type:code id: tags:
    ```
    lags.head()
    ```
    %% Cell type:code id: tags:
    ```
    raw_partitions = []
    filenames = [filename for filename in os.listdir(directory) if filename.startswith(f"exp{exp_id}") and filename.endswith("partitions.csv")]
    for filename in filenames:
    #print(filename)
    run_params = filename[:-4].split("_")
    dim_value = run_params[2]
    instances = run_params[3]
    df = pd.read_csv(os.path.join(directory, filename))
    #input = df.loc[df['topic'] == "input"]
    input = df
    #print(input)
    input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']
    #print(input)
    #print(input.iloc[0, 'timestamp'])
    input = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up
    #regress = input
    input = input.loc[input['topic'] >= 'input']
    mean = input['value'].mean()
    #input.plot(kind='line',x='timestamp',y='value',color='red')
    #plt.show()
    row = {'dim_value': int(dim_value), 'instances': int(instances), 'partitions': mean}
    #print(row)
    raw_partitions.append(row)
    partitions = pd.DataFrame(raw_partitions)
    #runs = lags.join(partitions.set_index(['dim_value', 'instances']), on=['dim_value', 'instances'])
    ```
    %% Cell type:code id: tags:
    ```
    raw_obs_instances = []
    filenames = [filename for filename in os.listdir(directory) if filename.startswith(f"exp{exp_id}") and filename.endswith("instances.csv")]
    for filename in filenames:
    run_params = filename[:-4].split("_")
    dim_value = run_params[2]
    instances = run_params[3]
    df = pd.read_csv(os.path.join(directory, filename))
    if df.empty:
    continue
    #input = df.loc[df['topic'] == "input"]
    input = df
    #print(input)
    input['sec_start'] = input.loc[0:, 'timestamp'] - input.iloc[0]['timestamp']
    #print(input)
    #print(input.iloc[0, 'timestamp'])
    input = input.loc[input['sec_start'] >= warmup_sec] # Warm-Up
    #regress = input
    #input = input.loc[input['topic'] >= 'input']
    #mean = input['value'].mean()
    #input.plot(kind='line',x='timestamp',y='value',color='red')
    #plt.show()
    #row = {'dim_value': int(dim_value), 'instances': int(instances), 'obs_instances': mean}
    #print(row)
    raw_obs_instances.append(row)
    obs_instances = pd.DataFrame(raw_obs_instances)
    obs_instances.head()
    ```
    %% Cell type:code id: tags:
    ```
    runs = lags
    #runs = lags.join(partitions.set_index(['dim_value', 'instances']), on=['dim_value', 'instances'])#.join(obs_instances.set_index(['dim_value', 'instances']), on=['dim_value', 'instances'])
    #runs["failed"] = runs.apply(lambda row: (abs(row['instances'] - row['obs_instances']) / row['instances']) > 0.1, axis=1)
    #runs.loc[runs['failed']==True]
    ```
    %% Cell type:code id: tags:
    ```
    #threshold = 1000
    # Set to true if the trend line has a slope less than
    runs["suitable"] = runs.apply(lambda row: row['trend_slope'] < threshold, axis=1)
    runs.columns = runs.columns.str.strip()
    runs.sort_values(by=["dim_value", "instances"])
    ```
    %% Cell type:code id: tags:
    ```
    filtered = runs[runs.apply(lambda x: x['suitable'], axis=1)]
    grouped = filtered.groupby(['dim_value'])['instances'].min()
    min_suitable_instances = grouped.to_frame().reset_index()
    min_suitable_instances
    ```
    %% Cell type:code id: tags:
    ```
    min_suitable_instances.to_csv(os.path.join(directory_out, f'../results-inst/exp{exp_id}_min-suitable-instances.csv'), index=False)
    min_suitable_instances.to_csv(os.path.join(directory_out, f'exp{exp_id}_min-suitable-instances.csv'), index=False)
    ```
    %% Cell type:code id: tags:
    ```
    min_suitable_instances.plot(kind='line',x='dim_value',y='instances')
    # min_suitable_instances.plot(kind='line',x='dim_value',y='instances', logy=True)
    plt.show()
    ```
    %% Cell type:code id: tags:
    ```
    ```
    ......
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