Improve model

challenge/api.py

@@ -126,9 +126,6 @@ async def post_predict(flight_data: FlightData) -> dict:
         preprocessed_data = model.preprocess(flight_data_df)
 
         # sorts column to feed the model
-        column_order = model._model.feature_names_in_
-        preprocessed_data = preprocessed_data[column_order]
-
         pred = model.predict(preprocessed_data)
 
         return {"predict": pred}
@@ -138,5 +135,5 @@ async def post_predict(flight_data: FlightData) -> dict:
         with open("error_logs.txt", "a") as f:
             f.write(f"{datetime.now(timezone.utc)}: encounter error {e}")
         raise HTTPException(
-            status_code=500, detail="Internal server error during prediction"
+            status_code=500, detail="Internal server error during prediction."
         )

challenge/model.py

@@ -6,61 +6,91 @@
 import numpy as np
 import pandas as pd
 from sklearn.linear_model import LogisticRegression
+from xgboost import XGBClassifier
 
-FEATURES_COLS = [
-    "OPERA_Latin American Wings",
-    "MES_7",
-    "MES_10",
-    "OPERA_Grupo LATAM",
-    "MES_12",
-    "TIPOVUELO_I",
-    "MES_4",
-    "MES_11",
-    "OPERA_Sky Airline",
-    "OPERA_Copa Air",
-]
 
+class DelayModel:
+    FEATURES_COLS = [
+        "MES_4",
+        "MES_7",
+        "MES_10",
+        "MES_11",
+        "MES_12",
+        "OPERA_Copa Air",
+        "OPERA_Grupo LATAM",
+        "OPERA_Latin American Wings",
+        "OPERA_Sky Airline",
+        "TIPOVUELO_I",
+    ]
+
+    THRESHOLD_IN_MINUTES = 15
 
-def get_min_diff(data):
-    fecha_o = datetime.strptime(data["Fecha-O"], "%Y-%m-%d %H:%M:%S")
-    fecha_i = datetime.strptime(data["Fecha-I"], "%Y-%m-%d %H:%M:%S")
-    min_diff = ((fecha_o - fecha_i).total_seconds()) / 60
-    return min_diff
+    def __init__(self):
+        self._model = LogisticRegression()
 
+    def _get_min_diff(self, data: pd.Series) -> float:
+        """
+        Auxiliary function to get target.
 
-def get_delay_target(data: pd.DataFrame) -> pd.Series:
-    data["min_diff"] = data.apply(get_min_diff, axis=1)
-    threshold_in_minutes = 15
-    data["delay"] = np.where(data["min_diff"] > threshold_in_minutes, 1, 0)
+        Args:
+            data (pd.Series): raw data row.
 
-    return data["delay"].to_frame()
+        Returns:
+            float: difference between two rows in minutes.
+        """
+        fecha_o = datetime.strptime(data["Fecha-O"], "%Y-%m-%d %H:%M:%S")
+        fecha_i = datetime.strptime(data["Fecha-I"], "%Y-%m-%d %H:%M:%S")
+        min_diff = ((fecha_o - fecha_i).total_seconds()) / 60
+        return min_diff
 
+    def _get_delay_target(self, data: pd.DataFrame) -> pd.Series:
+        """
+        Compute and return target to train the model with, from raw data.
 
-def get_features(data: pd.DataFrame) -> pd.DataFrame:
-    # get the one hot enconding of the columns suggested by the DS
-    features = pd.concat(
-        [
-            pd.get_dummies(data["OPERA"], prefix="OPERA"),
-            pd.get_dummies(data["TIPOVUELO"], prefix="TIPOVUELO"),
-            pd.get_dummies(data["MES"], prefix="MES"),
-        ],
-        axis=1,
-    )
-    present_features = list(set(FEATURES_COLS).intersection(set(features.columns)))
-    missing_features = list(set(FEATURES_COLS).difference(set(features.columns)))
+        Args:
+            data (pd.DataFrame): raw data.
 
-    features = features[present_features]
+        Returns:
+            pd.Series: target to predict.
+        """
+        data["min_diff"] = data.apply(self._get_min_diff, axis=1)
+        data["delay"] = np.where(data["min_diff"] > self.THRESHOLD_IN_MINUTES, 1, 0)
 
-    # fill missing features with 0 due to one-hot encoding of features
-    for feature in missing_features:
-        features[feature] = 0
+        return data["delay"].to_frame()
 
-    return features
+    def _get_features(self, data: pd.DataFrame) -> pd.DataFrame:
+        """
+        Compute and return input features to feed the model from raw data.
 
+        Args:
+            data (pd.DataFrame): raw_data.
 
-class DelayModel:
-    def __init__(self):
-        self._model = LogisticRegression()
+        Returns:
+            pd.DataFrame: features with columns in a specific order.
+        """
+        # get the one hot enconding of the columns suggested by the DS
+        # the existance of these three columns is enforced by the api above this code
+        features = pd.concat(
+            [
+                pd.get_dummies(data["OPERA"], prefix="OPERA"),
+                pd.get_dummies(data["TIPOVUELO"], prefix="TIPOVUELO"),
+                pd.get_dummies(data["MES"], prefix="MES"),
+            ],
+            axis=1,
+        )
+        valid_features = list(
+            set(self.FEATURES_COLS).intersection(set(features.columns))
+        )
+        missing_features = list(
+            set(self.FEATURES_COLS).difference(set(features.columns))
+        )
+
+        # get valid features and fill missin with  0 due to one-hot encoding
+        features = features[valid_features]
+        features[missing_features] = 0
+
+        # return dataframe with sorted columns
+        return features[self.FEATURES_COLS]
 
     def preprocess(
         self, data: pd.DataFrame, target_column: Optional[str] = None
@@ -78,20 +108,20 @@ def preprocess(
             pd.DataFrame: features.
         """
         # retrieve features from the data
-        x = get_features(data)
+        x = self._get_features(data)
 
         # return different sets, depending on the target
         if target_column is None:
             return x
         elif target_column == "delay":
-            y = get_delay_target(data)
+            y = self._get_delay_target(data)
             return (x, y)
         else:
             raise NotImplementedError("Only implemented 'delay' as target column")
 
     def fit(self, features: pd.DataFrame, target: pd.DataFrame) -> None:
         """
-        Fit model with preprocessed data.
+        Fit model with data preprocessed by this class.
 
         Args:
             features (pd.DataFrame): preprocessed data.
@@ -130,7 +160,7 @@ def load_model(self, path: str):
 
     def predict(self, features: pd.DataFrame) -> List[int]:
         """
-        Predict delays for new flights.
+        Predict delays for new flights on data preprocessed by this class.
 
         Args:
             features (pd.DataFrame): preprocessed data.
@@ -146,38 +176,20 @@ def predict(self, features: pd.DataFrame) -> List[int]:
         return pred
 
 
-if __name__ == "__main__":
-    from sklearn.metrics import classification_report, mean_squared_error
-    from sklearn.model_selection import train_test_split
+def main():
+    # perform a training of the model with all available data for production deployment
 
-    # perform a training of the model for production deployment
+    # get data and initial model
     model = DelayModel()
     data = pd.read_csv(filepath_or_buffer="data/data.csv")
 
+    # preprocess data and fit
     features, target = model.preprocess(data=data, target_column="delay")
-
-    _, features_validation, _, target_validation = train_test_split(
-        features, target, test_size=0.33, random_state=42
-    )
-
     model.fit(features=features, target=target)
 
-    predicted_target = model.predict(features_validation)
-
-    report = classification_report(
-        target_validation, predicted_target, output_dict=True
-    )
-
     # save
     model.save_model("models")
 
-    # re instantiate to override model
-    model = DelayModel()
-    model.load_model("models")
-
-    predicted_target_load = model.predict(features_validation)
 
-    print(
-        "The difference in prediction is:"
-        f" {mean_squared_error(predicted_target, predicted_target_load)}"
-    )
+if __name__ == "__main__":
+    main()