Upload 5 files

config.yaml

@@ -0,0 +1,33 @@
+
+SPM:
+  path_to_csv: "fixed.csv"
+  identifier_column: "Identifier"
+  sequence_column: "Sequence"
+  sortby: "S-Support"
+  sliding_window_min: 1
+  sliding_window_max: 4
+  min_gap: 1
+  max_gap: 12
+  S_support_thresh: 0.4
+  I_support_thresh: 0
+  dataset_format: 0
+
+DSM:
+  path_to_csv_left: "fixed.csv"
+  path_to_csv_right: "growth.csv"
+  identifier_column: "Identifier"
+  sequence_column: "Sequence"
+  sortby: "S-Support"
+  sliding_window_min: 1
+  sliding_window_max: 1
+  min_gap: 1
+  max_gap: 12
+  S_support_thresh: 0.4
+  I_support_thresh: 0
+  threshold_pvalue: 0.1
+  dataset_format: 0
+  test_type: "ttest_ind"
+  
+dsm_result_path: "/home/vivek.trivedi/ET623_project/dsm_result.csv"
+spm_result_path: "/home/vivek.trivedi/ET623_project/spm_result.csv"
+occurrence_matrix_path: "/home/vivek.trivedi/ET623_project/occurrence_matrix.csv"

fixed.csv

@@ -0,0 +1,16 @@
+Identifier,Sequence,timestamp
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growth.csv

@@ -0,0 +1,24 @@
+Identifier,Sequence,timestamp
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main.py

@@ -0,0 +1,79 @@
+import sys
+import yaml
+import pandas as pd
+from utils import *
+
+def load_config(filename):
+    with open(filename, 'r') as stream:
+        try:
+            return yaml.safe_load(stream)
+        except yaml.YAMLError as exc:
+            print(exc)
+
+def save_results(dsm_result, spm_result, occurrence_matrix, dsm_result_path, spm_result_path, occurrence_matrix_path):
+    dsm_result.to_csv(dsm_result_path, index=False)
+    spm_result.to_csv(spm_result_path, index=False)
+    occurrence_matrix.to_csv(occurrence_matrix_path, index=False)
+
+def main():
+    if len(sys.argv) > 1:
+        config_file = 'config.yaml'
+        command_args = sys.argv[1:]
+        overridden_params = {}
+        for arg in command_args:
+            if '=' in arg:
+                key, value = arg.split('=')
+                overridden_params[key] = value
+            else:
+                print(f"Ignoring invalid argument: {arg}")
+
+        config = load_config(config_file)
+
+        # SPM parameters
+        spm_params = config.get('SPM', {})
+        for key, value in overridden_params.items():
+            if key in spm_params:
+                spm_params[key] = value
+
+        spm_result, occurrence_matrix = SPM(spm_params)
+
+        # DSM parameters
+        dsm_params = config.get('DSM', {})
+        for key, value in overridden_params.items():
+            if key in dsm_params:
+                dsm_params[key] = value
+
+        ptrn_left, ptrn_right, ptrn_both_left, ptrn_both_right, dsm_result = DSM(dsm_params)
+
+        # Fetching paths from config
+        dsm_result_path = config.get('dsm_result_path')
+        spm_result_path = config.get('spm_result_path')
+        occurrence_matrix_path = config.get('occurrence_matrix_path')
+
+        # Saving results
+        save_results(dsm_result, spm_result, occurrence_matrix, dsm_result_path, spm_result_path, occurrence_matrix_path)
+
+    else:
+        config_file = 'config.yaml'
+        config = load_config(config_file)
+
+        # SPM parameters
+        spm_params = config.get('SPM', {})
+
+        spm_result, occurrence_matrix = SPM(spm_params)
+
+        # DSM parameters
+        dsm_params = config.get('DSM', {})
+
+        ptrn_left, ptrn_right, ptrn_both_left, ptrn_both_right, dsm_result = DSM(dsm_params)
+
+        # Fetching paths from config
+        dsm_result_path = config.get('dsm_result_path')
+        spm_result_path = config.get('spm_result_path')
+        occurrence_matrix_path = config.get('occurrence_matrix_path')
+
+        # Saving results
+        save_results(dsm_result, spm_result, occurrence_matrix, dsm_result_path, spm_result_path, occurrence_matrix_path)
+
+if __name__ == "__main__":
+    main()

utils.py

@@ -0,0 +1,276 @@
+import pandas as pd
+import numpy as np
+from scipy import stats
+import csv
+
+def process_csv(input_file, output_file="output.csv",user_id_column="user_id",timestamp_column="timestamp",action_column="actions"):
+
+    with open(input_file, 'r', newline='') as csvfile, open(output_file, 'w', newline='') as new_csvfile:
+        reader = csv.reader(csvfile)
+        writer = csv.writer(new_csvfile)
+
+        # Get the column indices for user_id, timestamp, and action
+        user_id_index, timestamp_index, action_index = None, None, None
+        for i, row in enumerate(reader):
+            if i == 0:  # Header row
+                user_id_index = row.index(user_id_column)
+                timestamp_index = row.index(timestamp_column)
+                action_index = row.index(action_column)
+                break
+
+        # Read the rest of the data and store it in a dictionary
+        user_actions = {}
+        for row in reader:
+            user_id = row[user_id_index]
+            timestamp = row[timestamp_index]
+            action = row[action_index]
+
+            if user_id not in user_actions:
+                user_actions[user_id] = []
+
+            # Sort actions by timestamp in ascending order
+            user_actions[user_id].append((timestamp, action))
+
+        # Write the data to the output CSV file with the new column
+        writer.writerow([user_id_column, 'actions'])  # Write header with new column
+        for user_id, actions in user_actions.items():
+            sorted_actions = actions  # Sort by timestamp
+            concatenated_actions = ';'.join([action for timestamp, action in sorted_actions])
+            writer.writerow([user_id,  concatenated_actions])
+
+
+def generate_sequence_list(sentence, min_gap,max_gap, sliding_window_min=1,sliding_window_max=1):
+    # Split the sentence into words
+    words = sentence.split(";")
+
+    # Generate n-grams
+    ngrams = []
+    for sliding_window in range(sliding_window_min,sliding_window_max+1):
+      for gram_length in range(min_gap,max_gap + 1):  # Loop from 1 to n
+          for i in range(0, len(words) - gram_length + 1, sliding_window):
+              ngram = '--->'.join(words[i:i+gram_length])
+              ngrams.append(ngram)
+
+    return ngrams
+    
+    
+    
+def create_dict_from_df(df, identifier_column, sequence,min_gap,max_gap,sliding_window_min=1,sliding_window_max=1):
+    result_dict = {}
+    unique_values_set = set()  # Initialize set to store unique values
+    for index, row in df.iterrows():
+        key = row[identifier_column]
+        values = generate_sequence_list(row[sequence],min_gap,max_gap,sliding_window_min,sliding_window_max)
+        result_dict[key] = values
+        unique_values_set.update(values)  # Update the set with unique values
+    return result_dict, unique_values_set
+    
+def create_dataframe_from_dict_and_set(result_dict, unique_values_set):
+    # Initialize an empty dictionary to store counts
+    counts_dict = {}
+    # Iterate over the set
+    for value in unique_values_set:
+        counts_dict[value] = {}
+        # Iterate over the keys in the result_dict
+        for key, values in result_dict.items():
+            counts_dict[value][key] = values.count(value)
+
+    # Create a DataFrame from the counts dictionary
+    df = pd.DataFrame(counts_dict).fillna(0)
+    # Transpose the DataFrame so that keys become columns and values become rows
+    df = df.transpose()
+
+    return df
+    
+    
+def process_dataframe(df):
+    # Calculate num_student
+    num_student = len(df.columns)
+
+    # Calculate I-Frequency and S-Frequency
+    I_Frequency = df.sum(axis=1)
+    S_Frequency = (df > 0).sum(axis=1)
+
+    # Create a dictionary for new data
+    new_data = {
+        'I-Frequency': I_Frequency,
+        'S-Frequency': S_Frequency
+    }
+
+    # Create a DataFrame from the new data
+    new_df = pd.DataFrame(new_data)
+
+    # Calculate I-Support by dividing I-Frequency with num_student
+    new_df['I-Support (mean)'] = new_df['I-Frequency'] / num_student
+    new_df['S-Support'] = new_df['S-Frequency'] / num_student
+
+    # Calculate standard deviation of each row
+    new_df['I-Support (sd)'] = df.std(axis=1)
+
+    return new_df
+
+
+def calculate_p_value(test_type, vector_a, vector_b=None, **kwargs):
+    """
+    Calculate the p-value for different types of t-tests.
+
+    Parameters:
+        test_type (str): Type of test to perform.
+        vector_a (array-like): Data for sample A.
+        vector_b (array-like, optional): Data for sample B (only required for some tests).
+        **kwargs: Additional keyword arguments required for specific tests.
+
+    Returns:
+        p_value (float): The p-value obtained from the test.
+    """
+    if test_type == 'poisson_means_test':
+        # Poisson means test
+        result = stats.poisson_means_test(vector_a, vector_b, **kwargs)
+    elif test_type == 'ttest_ind':
+        # T-test for the means of two independent samples
+        result = stats.ttest_ind(vector_a, vector_b, **kwargs)
+    elif test_type == 'mannwhitneyu':
+        # Mann-Whitney U rank test on two independent samples
+        result = stats.mannwhitneyu(vector_a, vector_b, **kwargs)
+    elif test_type == 'bws_test':
+        # Baumgartner-Weiss-Schindler test on two independent samples
+        result = stats.bws_test(vector_a, vector_b, **kwargs)
+    elif test_type == 'ranksums':
+        # Wilcoxon rank-sum statistic for two samples
+        result = stats.ranksums(vector_a, vector_b, **kwargs)
+    elif test_type == 'brunnermunzel':
+        # Brunner-Munzel test on samples
+        result = stats.brunnermunzel(vector_a, vector_b, **kwargs)
+    elif test_type == 'mood':
+        # Mood's test for equal scale parameters
+        result = stats.mood(vector_a, vector_b, **kwargs)
+    elif test_type == 'ansari':
+        # Ansari-Bradley test for equal scale parameters
+        result = stats.ansari(vector_a, vector_b, **kwargs)
+    elif test_type == 'cramervonmises_2samp':
+        # Two-sample Cram�r-von Mises test for goodness of fit
+        result = stats.cramervonmises_2samp(vector_a, vector_b, **kwargs)
+    elif test_type == 'epps_singleton_2samp':
+        # Epps-Singleton (ES) test statistic
+        result = stats.epps_singleton_2samp(vector_a, vector_b, **kwargs)
+    elif test_type == 'ks_2samp':
+        # Two-sample Kolmogorov-Smirnov test for goodness of fit
+        result = stats.ks_2samp(vector_a, vector_b, **kwargs)
+    elif test_type == 'kstest':
+        # One-sample or two-sample Kolmogorov-Smirnov test for goodness of fit
+        result = stats.kstest(vector_a, vector_b, **kwargs)
+    else:
+        raise ValueError("Invalid test type.")
+
+    # Get the p-value
+    p_value = result.pvalue
+    return p_value
+
+def SPM_(path_to_csv,dataset_format, identifier_column, sequence_column,sortby="S-Support",min_gap=1,max_gap=1,sliding_window_min=1,sliding_window_max=1,S_support_thresh=0,I_support_thresh=0,timestamp_column="timestamp"):
+
+    if dataset_format==1:
+      process_csv(path_to_csv, output_file="output.csv",user_id_column=identifier_column,timestamp_column=timestamp_column,action_column=sequence_column)
+      path_to_csv="output.csv"
+    # Read CSV file
+    data = pd.read_csv(path_to_csv)
+
+    # Create dictionary from DataFrame
+    data_seq, corpus = create_dict_from_df(data, identifier_column, sequence_column, min_gap,max_gap,sliding_window_min,sliding_window_max)
+
+    # Create occurrence matrix
+    occurence_matrix = create_dataframe_from_dict_and_set(data_seq, corpus)
+
+    # Process occurrence matrix
+    spm_result = process_dataframe(occurence_matrix)
+    spm_result = spm_result.sort_values(by=sortby, ascending=False)
+
+    return spm_result[(spm_result['S-Support'] > S_support_thresh) & (spm_result['I-Support (mean)'] > I_support_thresh)], occurence_matrix
+    
+
+
+def SPM(config):
+    path_to_csv = config.get('path_to_csv')
+    dataset_format = config.get('dataset_format')
+    identifier_column = config.get('identifier_column')
+    sequence_column = config.get('sequence_column')
+    sortby = config.get('sortby', "S-Support")
+    min_gap = config.get('min_gap', 1)
+    max_gap = config.get('max_gap', 1)
+    sliding_window_min = config.get('sliding_window_min', 1)
+    sliding_window_max = config.get('sliding_window_max', 1)
+    S_support_thresh = config.get('S_support_thresh', 0)
+    I_support_thresh = config.get('I_support_thresh', 0)
+    timestamp_column = config.get('timestamp_column', "timestamp")
+
+    return SPM_(path_to_csv,dataset_format, identifier_column, sequence_column,sortby,min_gap,max_gap,sliding_window_min,sliding_window_max,S_support_thresh,I_support_thresh,timestamp_column)
+
+
+
+def DSM(config):
+    path_to_csv_left = config['path_to_csv_left']
+    dataset_format = config['dataset_format']
+    path_to_csv_right = config['path_to_csv_right']
+    identifier_column = config['identifier_column']
+    sequence_column = config['sequence_column']
+    sortby = config['sortby']
+    min_gap = config['min_gap']
+    max_gap = config['max_gap']
+    sliding_window_min = config['sliding_window_min']
+    sliding_window_max = config['sliding_window_max']
+    S_support_thresh = config['S_support_thresh']
+    I_support_thresh = config['I_support_thresh']
+    threshold_pvalue = config['threshold_pvalue']
+    test_type = config['test_type']
+    timestamp_column = config.get('timestamp_column', 'timestamp')
+
+    if dataset_format == 1:
+        process_csv(path_to_csv_left, output_file="output_left.csv", user_id_column=identifier_column,
+                    timestamp_column=timestamp_column, action_column=sequence_column)
+        path_to_csv_left = "output_left.csv"
+        process_csv(path_to_csv_right, output_file="output_right.csv", user_id_column=identifier_column,
+                    timestamp_column=timestamp_column, action_column=sequence_column)
+        path_to_csv_left = "output_right.csv"
+
+    ptrn_left = []
+    ptrn_right = []
+    ptrn_both_left = []
+    ptrn_both_right = []
+
+    spm_result_left, occurence_matrix_left = SPM_(path_to_csv_left, 0, identifier_column, sequence_column, sortby,
+                                                 min_gap, max_gap, sliding_window_min, sliding_window_max,
+                                                 S_support_thresh, I_support_thresh)
+    spm_result_right, occurence_matrix_right = SPM_(path_to_csv_right, 0, identifier_column, sequence_column, sortby,
+                                                   min_gap, max_gap, sliding_window_min, sliding_window_max,
+                                                   S_support_thresh, I_support_thresh)
+
+    result_data = []
+    all_ptrn = set(spm_result_left.index)
+    all_ptrn.update(spm_result_right.index)
+    left_ptrn_data = set(spm_result_left.index)
+    right_ptrn_data = set(spm_result_right.index)
+
+    for ptrn in all_ptrn:
+        isupport_left = occurence_matrix_left.loc[ptrn, :].values if ptrn in spm_result_left.index else np.zeros(
+            occurence_matrix_left.shape[1])
+        isupport_right = occurence_matrix_right.loc[ptrn, :].values if ptrn in spm_result_right.index else np.zeros(
+            occurence_matrix_right.shape[1])
+        p_value = calculate_p_value(test_type, isupport_left, isupport_right)
+        if p_value < threshold_pvalue:
+            if (ptrn in left_ptrn_data) and (ptrn in right_ptrn_data):
+                if isupport_left.mean() > isupport_right.mean():
+                    ptrn_both_left.append(ptrn)
+                    result_data.append((ptrn, p_value, isupport_left.mean(), isupport_right.mean(), "both_left"))
+                else:
+                    ptrn_both_right.append(ptrn)
+                    result_data.append((ptrn, p_value, isupport_left.mean(), isupport_right.mean(), "both_right"))
+            else:
+                if ptrn in left_ptrn_data:
+                    ptrn_left.append(ptrn)
+                    result_data.append((ptrn, p_value, isupport_left.mean(), np.nan, "left"))
+                else:
+                    ptrn_right.append(ptrn)
+                    result_data.append((ptrn, p_value, np.nan, isupport_right.mean(), "right"))
+
+    result_df = pd.DataFrame(result_data,
+                             columns=['ptrn', 'ttest_value', 'isupportleft_mean', 'isupportright_mean', "Group"])
+    return ptrn_left, ptrn_right, ptrn_both_left, ptrn_both_right, result_df