Adding data loading for ptype to break dependence on that repo

mlguess/keras/data.py

@@ -0,0 +1,178 @@
+import pandas as pd
+import numpy as np
+import logging
+
+from sklearn.preprocessing import (
+    StandardScaler,
+    MinMaxScaler,
+    OneHotEncoder,
+    LabelEncoder,
+    RobustScaler,
+    QuantileTransformer,
+)
+
+from bridgescaler.group import GroupMinMaxScaler, GroupRobustScaler, GroupStandardScaler
+from sklearn.model_selection import GroupShuffleSplit
+
+
+logger = logging.getLogger(__name__)
+
+
+def load_ptype_uq(conf, data_split=0, verbose=0, drop_mixed=False):
+
+    # Load
+    df = pd.read_parquet(conf["data_path"])
+
+    # Drop mixed cases
+    if drop_mixed:
+        logger.info("Dropping data points with mixed observations")
+        c1 = df["ra_percent"] == 1.0
+        c2 = df["sn_percent"] == 1.0
+        c3 = df["pl_percent"] == 1.0
+        c4 = df["fzra_percent"] == 1.0
+        condition = c1 | c2 | c3 | c4
+        df = df[condition].copy()
+
+    # QC-Filter
+    qc_value = str(conf["qc"])
+    cond1 = df[f"wetbulb{qc_value}_filter"] == 0.0
+    cond2 = df["usa"] == 1.0
+    dg = df[cond1 & cond2].copy()
+
+    dg["day"] = dg["datetime"].apply(lambda x: str(x).split(" ")[0])
+    dg["id"] = range(dg.shape[0])
+
+    # Select test cases
+    test_days_c1 = dg["day"].isin(
+        [day for case in conf["case_studies"].values() for day in case]
+    )
+    test_days_c2 = dg["day"] >= conf["test_cutoff"]
+    test_condition = test_days_c1 | test_days_c2
+
+    # Partition the data into trainable-only and test-only splits
+    train_data = dg[~test_condition].copy()
+    test_data = dg[test_condition].copy()
+
+    # Make N train-valid splits using day as grouping variable, return "data_split" split
+    gsp = GroupShuffleSplit(
+        n_splits=conf["ensemble"]["n_splits"],
+        random_state=conf["seed"],
+        train_size=conf["train_size1"],
+    )
+    splits = list(gsp.split(train_data, groups=train_data["day"]))
+
+    train_index, valid_index = splits[data_split]
+    train_data, valid_data = (
+        train_data.iloc[train_index].copy(),
+        train_data.iloc[valid_index].copy(),
+    )
+
+    size = df.shape[0]
+    logger.info("Train, validation, and test fractions:")
+    logger.info(
+        f"{train_data.shape[0]/size}, {valid_data.shape[0]/size}, {test_data.shape[0]/size}"
+    )
+    data = {"train": train_data, "val": valid_data, "test": test_data}
+
+    return data
+
+
+def preprocess_data(
+    data,
+    input_features,
+    output_features,
+    scaler_type="standard",
+    encoder_type="onehot",
+    groups=[],
+    seed=1000,
+):
+    """
+    Function to select features and scale data for ML
+    Args:
+        data (dictionary of dataframes for training and validation data):
+        input_features (list): Input features
+        output_feature (list): Output feature
+        scaler_type: Type of scaling to perform (supports "standard" and "minmax")
+        encoder_type: Type of encoder to perform (supports "label" and "onehot")
+
+    Returns:
+        Dictionary of scaled and one-hot encoded data, dictionary of scaler objects
+    """
+    groupby = len(groups)
+
+    scalar_obs = {
+        "normalize": MinMaxScaler() if not groupby else GroupMinMaxScaler(),
+        "symmetric": MinMaxScaler((-1, 1))
+        if not groupby
+        else GroupMinMaxScaler(),
+        "standard": StandardScaler() if not groupby else GroupStandardScaler(),
+        "robust": RobustScaler() if not groupby else GroupRobustScaler(),
+        "quantile": QuantileTransformer(
+            n_quantiles=1000, random_state=seed, output_distribution="normal"
+        ),
+        "quantile-uniform": QuantileTransformer(
+            n_quantiles=1000, random_state=seed, output_distribution="uniform"
+        ),
+    }
+    scalers, scaled_data = {}, {}
+    scalers["input"] = scalar_obs[scaler_type]
+    scalers["output_label"] = LabelEncoder()
+    if encoder_type == "onehot":
+        scalers["output_onehot"] = OneHotEncoder(sparse_output=False)
+
+    if groupby and "quantile" not in scaler_type:
+        scaled_data["train_x"] = pd.DataFrame(
+            scalers["input"].fit_transform(
+                data["train"][input_features], groups=groups
+            ),
+            columns=input_features,
+        )
+    else:
+        scaled_data["train_x"] = pd.DataFrame(
+            scalers["input"].fit_transform(data["train"][input_features]),
+            columns=input_features,
+        )
+    scaled_data["val_x"] = pd.DataFrame(
+        scalers["input"].transform(data["val"][input_features]), columns=input_features
+    )
+    scaled_data["test_x"] = pd.DataFrame(
+        scalers["input"].transform(data["test"][input_features]), columns=input_features
+    )
+    if "left_overs" in data:
+        scaled_data["left_overs_x"] = pd.DataFrame(
+            scalers["input"].transform(data["left_overs"][input_features]),
+            columns=input_features,
+        )
+
+    scalers["output_label"] = LabelEncoder()
+    scaled_data["train_y"] = scalers["output_label"].fit_transform(
+        np.argmax(data["train"][output_features].to_numpy(), 1)
+    )
+    scaled_data["val_y"] = scalers["output_label"].transform(
+        np.argmax(data["val"][output_features].to_numpy(), 1)
+    )
+    scaled_data["test_y"] = scalers["output_label"].transform(
+        np.argmax(data["test"][output_features].to_numpy(), 1)
+    )
+    if "left_overs" in data:
+        scaled_data["left_overs_y"] = scalers["output_label"].transform(
+            np.argmax(data["left_overs"][output_features].to_numpy(), 1)
+        )
+
+    if encoder_type == "onehot":
+        scalers["output_onehot"] = OneHotEncoder(sparse_output=False)
+        scaled_data["train_y"] = scalers["output_onehot"].fit_transform(
+            np.expand_dims(scaled_data["train_y"], 1)
+        )
+        scaled_data["val_y"] = scalers["output_onehot"].transform(
+            np.expand_dims(scaled_data["val_y"], 1)
+        )
+        scaled_data["test_y"] = scalers["output_onehot"].transform(
+            np.expand_dims(scaled_data["test_y"], 1)
+        )
+        if "left_overs" in data:
+            scaled_data["left_overs_y"] = scalers["output_onehot"].transform(
+                np.expand_dims(scaled_data["left_overs_y"], 1)
+            )
+
+    return scaled_data, scalers