diff --git a/CMFS_20NG.ipynb b/CMFS_20NG.ipynb index 0b714ca..b8b8274 100644 --- a/CMFS_20NG.ipynb +++ b/CMFS_20NG.ipynb @@ -30,6 +30,7 @@ "import matplotlib.pyplot as plt\n", "import numpy as np\n", "import operator\n", + "import math\n", "%matplotlib inline" ] }, @@ -109,19 +110,20 @@ "name": "stdout", "output_type": "stream", "text": [ - "Term-category matrix shape: (101323, 20)\n" + "Term-category matrix shape: (101323, 20) \n", + "\n" ] } ], "source": [ - "print \"Term-category matrix shape: {0}\".format(term_category_mat.shape)" + "print \"Term-category matrix shape: {0} \\n\".format(term_category_mat.shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "### Perform CMFS on and iCMFS term-category matrix" + "### Perform CMFS, iCMFS and CC term-category matrix" ] }, { @@ -132,6 +134,7 @@ }, "outputs": [], "source": [ + "cc_term_category_mat = np.copy(term_category_mat)\n", "icmfs_term_category_mat = np.copy(term_category_mat)\n", "cmfs_term_category_mat = np.copy(term_category_mat)" ] @@ -147,19 +150,32 @@ "name": "stdout", "output_type": "stream", "text": [ - "[[ 1.15911720e-08 2.55417642e-06 4.92091096e-07 ..., 1.10450006e-07\n", - " 8.09032111e-07 4.95452140e-08]\n", - " [ 5.29700119e-07 7.53707062e-07 6.06857550e-08 ..., 9.89292091e-06\n", - " 3.01809142e-06 5.66036501e-07]\n", - " [ 1.12906601e-07 9.71858214e-08 3.96143123e-08 ..., 1.72138380e-07\n", - " 7.88057204e-08 1.20651771e-07]\n", + "[[-1.78838352 1.13194884 -1.17605256 ..., -1.8480978 -0.40521161\n", + " -1.51719869]\n", + " [-0.61103784 -0.42400297 -2.15122478 ..., 3.48088231 1.17184346\n", + " -0.51875683]\n", + " [ 0.94644767 0.83129307 0.294858 ..., 1.09212797 0.68361091\n", + " 1.0000758 ]\n", " ..., \n", - " [ 1.45165630e-07 1.24953199e-07 5.09326872e-08 ..., 5.53301937e-08\n", - " 1.01321641e-07 1.55123706e-07]\n", - " [ 1.45165630e-07 1.24953199e-07 5.09326872e-08 ..., 5.53301937e-08\n", - " 1.01321641e-07 1.55123706e-07]\n", - " [ 1.45165630e-07 1.24953199e-07 5.09326872e-08 ..., 5.53301937e-08\n", - " 1.01321641e-07 1.55123706e-07]]\n", + " [ 3.27637045 3.00309585 1.75281203 ..., 1.84416838 2.65497586\n", + " 3.40416536]\n", + " [ 3.27637045 3.00309585 1.75281203 ..., 1.84416838 2.65497586\n", + " 3.40416536]\n", + " [ 3.27637045 3.00309585 1.75281203 ..., 1.84416838 2.65497586\n", + " 3.40416536]]\n", + "[[ 7.74349505e-07 1.70632033e-04 3.28741992e-05 ..., 7.37862469e-06\n", + " 5.40474781e-05 3.30987341e-06]\n", + " [ 3.53866740e-05 5.03514822e-05 4.05411846e-06 ..., 6.60897657e-04\n", + " 2.01623925e-04 3.78141300e-05]\n", + " [ 7.54273777e-06 6.49250937e-06 2.64643844e-06 ..., 1.14997232e-05\n", + " 5.26462472e-06 8.06015469e-06]\n", + " ..., \n", + " [ 9.69780571e-06 8.34751204e-06 3.40256371e-06 ..., 3.69633960e-06\n", + " 6.76880321e-06 1.03630560e-05]\n", + " [ 9.69780571e-06 8.34751204e-06 3.40256371e-06 ..., 3.69633960e-06\n", + " 6.76880321e-06 1.03630560e-05]\n", + " [ 9.69780571e-06 8.34751204e-06 3.40256371e-06 ..., 3.69633960e-06\n", + " 6.76880321e-06 1.03630560e-05]]\n", "[[ 2.92719610e-08 7.28597222e-06 2.78309396e-06 ..., 5.88282467e-07\n", " 2.72656836e-06 1.18472355e-07]\n", " [ 1.33768710e-06 2.15000369e-06 3.43217261e-07 ..., 5.26920017e-05\n", @@ -177,21 +193,28 @@ } ], "source": [ + "term_freq = np.sum(term_category_mat)\n", "term_freq_per_cat = np.cumsum(term_category_mat, axis=0)[-1, :]\n", + "\n", "for term in range(terms):\n", " # Frequency of the term across all categories\n", - " # CMFS(tk,ci) = (P(tk|ci)*P(ci|tk))/P(ci)\n", + " # ICMFS(tk,ci) = (P(tk|ci)*P(ci|tk))/P(ci)\n", " total_term_freq = sum(term_category_mat[term, :])\n", " for cat in range(categories):\n", - " icmfs_numerator = float(((icmfs_term_category_mat[term][cat] + 1) ** 2) * documents)\n", - " icmfs_denominator = (total_term_freq + categories) * (term_freq_per_cat[cat] + terms) * term_freq_per_cat[cat]\n", - " icmfs_term_category_mat[term][cat] = icmfs_numerator / icmfs_denominator\n", - "\n", - " cmfs_numerator = float((cmfs_term_category_mat[term][cat] + 1) ** 2)\n", - " cmfs_denominator = (total_term_freq + categories) * (term_freq_per_cat[cat] + terms)\n", - " cmfs_term_category_mat[term][cat] = cmfs_numerator / cmfs_denominator \n", + " p_ci = float((term_freq_per_cat[cat] / term_freq))\n", + " p_tk = float((total_term_freq / term_freq))\n", + " p_ci_tk = float(term_category_mat[term][cat] + 1) / (total_term_freq + categories)\n", + " p_tk_ci = float(term_category_mat[term][cat] + 1) / (term_freq_per_cat[cat] + terms)\n", + " p_ntk_nci = float(term_freq - total_term_freq - term_freq_per_cat[cat] + term_category_mat[term][cat] + 1) / (term_freq - term_freq_per_cat[cat] + terms)\n", + " p_tk_nci = float(total_term_freq - term_category_mat[term][cat] - 1) / (term_freq - term_freq_per_cat[cat] + terms)\n", + " p_ntk_ci = float(term_freq_per_cat[cat] - term_category_mat[term][cat] - 1) / (term_freq_per_cat[cat] + terms)\n", " \n", + " cc_term_category_mat[term][cat] = (math.sqrt(documents) * ((p_tk_ci * p_ntk_nci) - (p_tk_nci * p_ntk_ci))) / math.sqrt(p_tk * (1-p_tk) * p_ci * (1-p_ci))\n", + " icmfs_term_category_mat[term][cat] = p_ci_tk * p_tk_ci / p_ci\n", + " cmfs_term_category_mat[term][cat] = p_ci_tk * p_tk_ci\n", + " \n", "# Final CMFS matrix\n", + "print cc_term_category_mat\n", "print icmfs_term_category_mat\n", "print cmfs_term_category_mat" ] @@ -236,9 +259,6 @@ }, "outputs": [], "source": [ - "# Integer to term mapping dictionary\n", - "dictionary = vec.get_feature_names()\n", - "\n", "sorted_feature_list_cmfs = sorted(term_cmfs_dict.items(), key=operator.itemgetter(1), reverse=True)[:2000]\n", "sorted_feature_list_icmfs = sorted(term_icmfs_dict.items(), key=operator.itemgetter(1), reverse=True)[:2000]" ] @@ -394,9 +414,9 @@ "outputs": [ { "data": { - "image/png": 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d6ZVX4NIlGF/tPWRzLHz/vatD8DjHzh3jvT/fY9qWafS7qR+7nt9F5bKV3R2W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\nSimVjSYHpZRS2fw/kNI/hXVBIasAAAAASUVORK5CYII=\n", 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RxR2Io0yJMpmJoEmVJvRo0oPGVRpTo3wNvzQDBSOf90mIyFYgCUgHTqlqGxEZCjwA7HMd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\nkMtNn8QbbtupwDZ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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -411,6 +431,22 @@ "plt.legend((\"ICMFS\", \"chi2\", \"CMFS\"), loc='best')\n", "plt.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plug in IGFSS" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { @@ -429,7 +465,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", - "version": "2.7.10" + "version": "2.7.11" } }, "nbformat": 4,