approach_pipeline.ipynb

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    "#reload source files automatically\n",
    "%load_ext autoreload\n",
    "%autoreload 2"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from tensorflow.keras.callbacks import EarlyStopping\n",
    "from webtable_recognition.loader import load_from_directory\n",
    "from webtable_recognition.transformer import transform_for_approach\n",
    "from webtable_recognition.splitter import split_fixed, split_random, series_to_sequence\n",
    "from webtable_recognition.classifier import create_nn_classifier\n",
    "from webtable_recognition.evaluator import evaluation_report"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load raw data to dataframe"
   ]
  },
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   "outputs": [],
   "source": [
    "raw = load_from_directory('./data/dataset_labelled/')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Generate feature vectors for each sample in raw dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "approach_df = transform_for_approach(raw, strategy='char_blocks', resize_mode='resize')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Split dataset into test and train data according to previously computed indices"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "source": [
    "train_X, train_Y, test_X, test_Y = split_fixed(approach_df, './data/split_idx.npz')\n",
    "\n",
    "train_seq = series_to_sequence(train_X, train_Y, batch_size=64)\n",
    "test_seq = series_to_sequence(test_X, test_Y, batch_size=64)"
   ]
  },
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   "cell_type": "markdown",
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   "source": [
    "### Learn neural net model w/ epoch-wise evaluation"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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    "earlyStopping = EarlyStopping(monitor='val_loss', patience=10, verbose=1, restore_best_weights=True)\n",
    "\n",
    "clf = create_nn_classifier()\n",
    "clf.fit_generator(train_seq,\n",
    "                  epochs=40,\n",
    "                  validation_data=test_seq,\n",
    "                  callbacks=[earlyStopping],\n",
    "                  validation_freq=1,\n",
    "                  verbose=1)"
   ]
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "y_pred = np.eye(4, dtype=int)[np.argmax(clf.predict_generator(test_seq), axis=1)]\n",
    "y_true = test_seq.y\n",
    "\n",
    "evaluation_report(y_true, y_pred, classes=test_seq.classes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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