Add Homo LR files

Signed-off-by: cwj <talkingwallace@sohu.com>
Signed-off-by: weiwee <wbwmat@gmail.com>

python/fate/ml/glm/homo_lr/client.py

@@ -0,0 +1,54 @@
+from typing import Optional
+from fate.arch import Context
+from fate.arch.dataframe import DataFrame
+from fate.ml.abc.module import HomoModule
+from fate.arch import Context
+import logging
+import pandas as pd
+
+
+logger = logging.getLogger(__name__)
+
+
+class Data(object):
+
+    def __init__(self, features: pd.DataFrame, sample_ids: pd.DataFrame, match_ids: pd.DataFrame, labels: pd.DataFrame) -> None:
+        # set var
+        self.features = features
+        self.sample_ids = sample_ids
+        self.match_ids = match_ids
+        self.labels = labels
+
+    @staticmethod
+    def from_fate_dataframe(df: DataFrame):
+        schema = df.schema
+        sample_id = schema.sample_id_name
+        match_id = schema.match_id_name
+        label = schema.label_name
+        pd_df = df.as_pd_df()
+        features = pd_df.drop([sample_id, match_id, label], axis=1)
+        sample_ids = pd_df[[sample_id]]
+        match_ids = pd_df[[match_id]]
+        labels = pd_df[[label]]
+        return Data(features, sample_ids, match_ids, labels)
+
+
+class HomoLRClient(HomoModule):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self.df_schema = None
+        self.train_data = None
+        self.validate_data = None
+        self.predict_data = None
+
+    def fit(self, ctx: Context, train_data: DataFrame, validate_data: DataFrame = None) -> None:
+
+        train_pd_df = DataFrame.as_pd_df()
+        if validate_data is not None:
+            validate_pd_df = DataFrame.as_pd_df()
+
+
+
+    def predict(self, ctx: Context, predict_data: DataFrame) -> DataFrame:
+        return super().predict(ctx, predict_data)