teste

Train_model/ml.py

@@ -1,57 +1,57 @@
-import pickle
-import pandas as pd
-import requests
-from io import StringIO
-from mlxtend.frequent_patterns import apriori, association_rules
-
-URL = "https://homepages.dcc.ufmg.br/~cunha/hosted/cloudcomp-2023s2-datasets/2023_spotify_ds1.csv"
-RULES_PATH = "models/rules.pkl"
-FREK_PATH = "models/freq.pkl"
-
-min_support = 0.04
-min_threshold = 1
-
-class FreqDatasetMining:
-    def __init__(self, file):
-        self.file = file
-        self.df = None
-        self.freq = None
-        self.rules = None
+# import pickle
+# import pandas as pd
+# import requests
+# from io import StringIO
+# from mlxtend.frequent_patterns import apriori, association_rules
+
+# URL = "https://homepages.dcc.ufmg.br/~cunha/hosted/cloudcomp-2023s2-datasets/2023_spotify_ds1.csv"
+# RULES_PATH = "models/rules.pkl"
+# FREK_PATH = "models/freq.pkl"
+
+# min_support = 0.04
+# min_threshold = 1
+
+# class FreqDatasetMining:
+#     def __init__(self, file):
+#         self.file = file
+#         self.df = None
+#         self.freq = None
+#         self.rules = None
 
-    def group_by_pid_and_track_uri(self):
-        df = self.file
+#     def group_by_pid_and_track_uri(self):
+#         df = self.file
 
-        df = df.groupby(['pid', 'track_name'])['track_name'].count().unstack().fillna(0)
+#         df = df.groupby(['pid', 'track_name'])['track_name'].count().unstack().fillna(0)
 
-        df = df.applymap(self.encode)
+#         df = df.applymap(self.encode)
 
-        self.df = df
+#         self.df = df
 
-    def encode(self, x):
-        if x <= 0:
-            return False
-        if x >= 1:
-            return True
+#     def encode(self, x):
+#         if x <= 0:
+#             return False
+#         if x >= 1:
+#             return True
 
-    def get_freq(self):
-        self.freq = apriori(self.df, min_support=min_support, use_colnames=True, verbose=1)
-        self.freq.to_pickle(FREK_PATH)
+#     def get_freq(self):
+#         self.freq = apriori(self.df, min_support=min_support, use_colnames=True, verbose=1)
+#         self.freq.to_pickle(FREK_PATH)
 
-    def get_rules(self):
-        self.rules = association_rules(self.freq, metric="lift", min_threshold=min_threshold)
-        self.rules.to_pickle(RULES_PATH)
+#     def get_rules(self):
+#         self.rules = association_rules(self.freq, metric="lift", min_threshold=min_threshold)
+#         self.rules.to_pickle(RULES_PATH)
 
-    # Keep the container running forever
-    while True:
-        print("Running...")
-        time.sleep(3)
-
-
-if __name__ == '__main__':
-    response = requests.get(URL, verify=False)
-    csv_data = StringIO(response.text)
-    df = pd.read_csv(csv_data)
-    fdm = FreqDatasetMining(df)
-    fdm.group_by_pid_and_track_uri()
-    fdm.get_freq()
-    fdm.get_rules()
+#     # Keep the container running forever
+#     while True:
+#         print("Running...")
+#         time.sleep(3)
+
+
+# if __name__ == '__main__':
+#     response = requests.get(URL, verify=False)
+#     csv_data = StringIO(response.text)
+#     df = pd.read_csv(csv_data)
+#     fdm = FreqDatasetMining(df)
+#     fdm.group_by_pid_and_track_uri()
+#     fdm.get_freq()
+#     fdm.get_rules()