03_similarity_feature_importance.ipynb

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   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"C:/Users/Salif SAWADOGO/OneDrive - EQUATORIAL COCA-COLA BOTTLING COMPANY S.L/dynamic segmentation/matching/output/training_set_matching.csv\")\n",
    "df"
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   "cell_type": "code",
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  {
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   "source": [
    "df.apply(lambda x: print(x), axis=1)"
   ]
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   "cell_type": "code",
   "execution_count": null,
   "id": "54b7608f",
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   "source": [
    "df[\"index\"] = df.apply(lambda x: \"{}_{}\".format(x[\"L_CUSTOMER_ID\"], x[\"R_id\"]), axis=1)\n",
    "df[\"dist_similarity\"] = (1000-df[\"dist_m\"])/1000\n",
    "df[[\"index\", \"dist_similarity\", \"name_similarity\", \"address_similarity\", \"match_status\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "84767cc8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "y = df[\"match_status\"]\n",
    "X = df[[\"name_similarity\", \"address_similarity\"]]\n",
    "clf = LogisticRegression(random_state=0).fit(X, y)\n",
    "clf.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "561dd007",
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   "source": [
    "sdf = spark.createDataFrame(df)\n",
    "sdf.show()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97dce5e8",
   "metadata": {},
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   "source": [
    "from pyspark.ml.classification import LogisticRegression\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "import pyspark.sql.functions as F\n",
    "lr = LogisticRegression()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98ac85f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "training = sdf[[\"index\", \"dist_similarity\", \"name_similarity\", \"address_similarity\", \"match_status\"]].withColumn(\"label\", F.col(\"match_status\"))\n",
    "assembler = VectorAssembler(inputCols=[\"name_similarity\", \"address_similarity\"], outputCol=\"features\")\n",
    "training_ready = assembler.transform(training)\n",
    "training_ready.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cf8faaaa",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_fit = lr.fit(training_ready)\n",
    "model_fit.coefficients / sum(model_fit.coefficients)"
   ]
  }
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