From 3001b50822d6ae7146f961f43174fda2f26b5ab1 Mon Sep 17 00:00:00 2001 From: smo216 <44307229+smo216@users.noreply.github.com> Date: Wed, 2 Oct 2019 12:42:50 -0400 Subject: [PATCH] some dumb line --- Machine Learning Final Project DTA data.ipynb | 262 +++++++++++++++++- 1 file changed, 261 insertions(+), 1 deletion(-) diff --git a/Machine Learning Final Project DTA data.ipynb b/Machine Learning Final Project DTA data.ipynb index 47b2021..8ea8145 100644 --- a/Machine Learning Final Project DTA data.ipynb +++ b/Machine Learning Final Project DTA data.ipynb @@ -1 +1,261 @@ -{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"name":"Machine Learning Final Project DTA data.ipynb","provenance":[{"file_id":"1k987y4Sj4mvueMqj9d-InjiUeK1611Yn","timestamp":1569961189320}]},"kernelspec":{"name":"python3","display_name":"Python 3"}},"cells":[{"cell_type":"code","metadata":{"id":"hsd_fYYqohhi","colab_type":"code","outputId":"3492c78a-55b9-42d4-9201-1a12f1e3c211","executionInfo":{"status":"ok","timestamp":1570032980054,"user_tz":240,"elapsed":102901,"user":{"displayName":"Sean Orzolek","photoUrl":"","userId":"13907533065611641698"}},"colab":{"base_uri":"https://localhost:8080/","height":1000}},"source":["from google.colab import drive\n","drive.mount('/content/gdrive')\n","import matplotlib.pyplot as plt\n","import numpy as np\n","import pandas as pd\n","# importing os module \n","import os \n","from sklearn.ensemble import RandomForestClassifier\n","from sklearn.datasets import make_classification\n","\n","#Load in data\n","os.chdir(\"//content//gdrive//My Drive//PhD casting project//Summary files\")\n","\n","filename='DTAdatatrimtrain.csv'\n","filename1='DTAdatatrimtest.csv'\n","\n","data = pd.read_csv(filename)\n","data.head()\n","data.info()\n","tdata = pd.read_csv(filename1)\n","#F_MC=data['Fraction MC E1']\n","\n","## Define Data of Interest\n","labels=data['Sample ID']\n","liquidus=data['Liquidus']\n","tliquidus=tdata['Liquidus']\n","comp=data.loc[:,'C':'Fe']\n","tcomp=tdata.loc[:,'C':'Fe']\n","print(comp)\n","print(liquidus)"],"execution_count":1,"outputs":[{"output_type":"stream","text":["Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n","\n","Enter your authorization code:\n","··········\n","Mounted at /content/gdrive\n","\n","RangeIndex: 27 entries, 0 to 26\n","Data columns (total 16 columns):\n","Sample ID 27 non-null object\n","C 27 non-null float64\n","Cr 27 non-null float64\n","Ni 27 non-null float64\n","Mn 27 non-null float64\n","Si 27 non-null float64\n","Mo 27 non-null float64\n","Nb 27 non-null float64\n","W 27 non-null float64\n","Ti 27 non-null float64\n","Zr 27 non-null float64\n","Fe 27 non-null float64\n","Liquidus 27 non-null float64\n","Te1 27 non-null object\n","Te2 27 non-null float64\n","STR 27 non-null float64\n","dtypes: float64(14), object(2)\n","memory usage: 3.5+ KB\n"," C Cr Ni Mn Si ... Nb W Ti Zr Fe\n","0 0.485 23.245 33.761 0.972 0.765 ... 0.393 0.1050 0.0660 0.00 39.6340\n","1 0.440 23.632 34.191 0.913 0.773 ... 0.380 0.1380 0.0670 0.00 38.9870\n","2 0.450 26.732 34.639 0.829 1.503 ... 1.036 0.0000 0.0770 0.00 34.6500\n","3 0.410 25.940 35.640 0.960 2.000 ... 1.110 0.0900 0.0300 0.12 33.1500\n","4 0.410 25.690 36.040 0.900 1.900 ... 1.110 0.0900 0.0400 0.14 33.1100\n","5 0.420 25.390 35.710 0.950 2.000 ... 1.110 0.0800 0.0400 0.10 33.5900\n","6 0.430 25.000 35.000 0.600 1.300 ... 0.800 0.1260 0.1300 0.19 36.0475\n","7 0.430 25.000 34.000 0.600 1.300 ... 1.110 0.0910 0.1300 0.17 36.8251\n","8 0.450 26.000 35.000 0.700 1.300 ... 0.800 0.1130 0.1300 0.18 35.6654\n","9 0.440 24.930 34.000 1.260 1.380 ... 1.300 0.0000 0.0000 0.00 36.6100\n","10 0.326 24.700 34.400 0.912 1.120 ... 0.404 0.0878 0.0057 0.00 37.7624\n","11 0.523 25.200 35.000 0.899 1.140 ... 0.418 0.0879 0.0064 0.00 36.4539\n","12 0.343 24.290 34.420 0.945 1.210 ... 0.994 0.0425 0.0065 0.00 37.5269\n","13 0.514 25.800 34.700 0.932 1.160 ... 1.150 0.0387 0.0074 0.00 35.4800\n","14 0.336 25.200 34.600 0.901 1.140 ... 1.470 0.0305 0.0081 0.00 36.0724\n","15 0.554 25.500 34.500 0.942 1.150 ... 1.480 0.0336 0.0087 0.00 35.6089\n","16 0.499 25.000 35.400 1.120 0.807 ... 0.978 0.0181 0.0081 0.00 35.9586\n","17 0.425 25.500 34.900 0.848 1.310 ... 0.922 0.0379 0.0080 0.00 35.8201\n","18 0.445 25.400 34.100 0.933 2.570 ... 1.010 0.0685 0.0095 0.00 35.1628\n","19 0.407 24.800 34.100 0.923 1.120 ... 1.610 0.0317 0.1140 0.00 36.6350\n","20 0.430 25.000 34.000 0.790 1.200 ... 0.910 1.5900 0.1380 0.00 35.9922\n","21 0.448 24.800 34.700 0.853 1.050 ... 1.440 1.4500 0.0195 0.00 34.9732\n","22 0.450 25.000 34.000 0.940 1.200 ... 0.960 0.0910 0.1300 0.00 37.3938\n","23 0.430 25.000 34.000 0.990 1.210 ... 1.500 1.5400 0.1560 0.00 35.4232\n","24 0.424 25.200 34.000 0.892 1.220 ... 1.450 0.0628 0.0091 0.00 36.4819\n","25 0.452 24.900 34.400 0.900 1.120 ... 0.944 0.0342 0.0084 0.00 36.9844\n","26 0.430 25.300 34.700 0.976 1.150 ... 0.506 0.0638 0.0070 0.00 36.5904\n","\n","[27 rows x 11 columns]\n","0 1382.850000\n","1 1382.400000\n","2 1362.550000\n","3 1351.600000\n","4 1352.150000\n","5 1352.350000\n","6 1367.550000\n","7 1370.400000\n","8 1370.650000\n","9 1359.900000\n","10 1383.300000\n","11 1368.400000\n","12 1375.400000\n","13 1361.500000\n","14 1366.000000\n","15 1356.700000\n","16 1371.900000\n","17 1365.200000\n","18 1348.850000\n","19 1364.050000\n","20 1366.150000\n","21 1361.766667\n","22 1369.600000\n","23 1357.050000\n","24 1367.800000\n","25 1368.200000\n","26 1374.800000\n","Name: Liquidus, dtype: float64\n"],"name":"stdout"}]},{"cell_type":"code","metadata":{"id":"tbBL8aI11Oll","colab_type":"code","colab":{}},"source":["# %% Visualize Data\n","plt.figure(figsize=(10,6))\n","plt.scatter(data['C'],data['Liquidus'])\n","plt.xlabel('Concentration (wt.%)')\n","plt.ylabel('Temperature (C)')\n","plt.show()"],"execution_count":0,"outputs":[]},{"cell_type":"code","metadata":{"id":"7slpzDth25jB","colab_type":"code","outputId":"e2537815-fdac-4eb6-d12e-cd2a9ba41e9d","executionInfo":{"status":"ok","timestamp":1570032987276,"user_tz":240,"elapsed":584,"user":{"displayName":"Sean Orzolek","photoUrl":"","userId":"13907533065611641698"}},"colab":{"base_uri":"https://localhost:8080/","height":422}},"source":[" # Trees!\n","\n","from sklearn.ensemble import RandomForestRegressor\n","from sklearn.datasets import make_regression\n","\n","X=comp\n","y=liquidus\n","#X, y = make_regression(n_features=4, n_informative=2,\n","# random_state=0, shuffle=False)\n","\n","regr = RandomForestRegressor(max_depth=20, random_state=0,\n"," n_estimators=100)\n","\n","regr.fit(X, y) \n","\n","print(regr.feature_importances_)\n","\n","print(regr.predict(tcomp))\n","print(tliquidus)\n","\n","plt.scatter(tliquidus,regr.predict(tcomp))\n","plt.plot([1350,1400],[1350,1400])\n","plt.ylim(1350,1400)\n","plt.xlim(1350,1400)\n","plt.xlabel('Measured Temperature (C)')\n","plt.ylabel('Predicted Temperature (C)')"],"execution_count":3,"outputs":[{"output_type":"stream","text":["[0.01670288 0.08567572 0.00881047 0.03942633 0.13336387 0.02787641\n"," 0.18501999 0.01289004 0.00702579 0.00138209 0.4818264 ]\n","[1368.957 1352.84250002 1367.56650001]\n","0 1362.80\n","1 1357.85\n","2 1363.90\n","Name: Liquidus, dtype: float64\n"],"name":"stdout"},{"output_type":"execute_result","data":{"text/plain":["Text(0, 0.5, 'Predicted Temperature (C)')"]},"metadata":{"tags":[]},"execution_count":3},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAZgAAAEKCAYAAAAvlUMdAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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DZz/5KfM\nXrSVYQM68O7Q05VcRIqQaM5grgB6AN8BhHfeqxJgKZF9nSW3UWSFPcps455U7pmczKfLttOrRW0e\nvao7betXi9n+RCR/okkwae7uZuYAZlY1xjFJETO4R5McE0ZhjjLLynLe+Hotj85cggP3XdaZm/q1\npIyKU4oUSdEkmH+a2f8BtczslwRTKf8ttmFJcVFYo8xWbj/AyMQkvl2zmzPb1eXhK7rRrI6KU4oU\nZdGMInvCzC4A9hHc1f8nd58d88ikWIj1KLOMzCwmfLaKpz9cTqVyZXj86u5c3UvFKUWKgxMmGDN7\n1N1HALOP0yalXCxHmaVs2suIxCQWbtzHwC4NeWBwF+pXV3FKkeIimvlgLzhO20UFHYgUT7EYZXY4\nPZPHZy3h8ue+YMveI/z1hp68eGMvJReRYia3asq/An4NtDGzpIinqgNfxjowKR6iGWWWF3PX7GJ4\nYhKrth/k6l5NufeSTtSqouKUIsVRjvPBhKVbagOPACMjntqfn2KUhUnzwRQ/B48ExSlf+2oNjWtW\n5pEru3FW+3rxDkukVCm0+WDcfS+w18yeAXZFVFOuYWZ93f3rggpCSrc5y7YzanIym/amcnO/lgwb\n0IGqKk4pUuxF87/4r0DPiOUDx2kTybM9h9J4cMZiJs3bQOt6VXnnzn4ktFRxSpGSIpoEYx7Rj+bu\nWWamr5fyk8xM3syYaSnsPpTG/5zTht+cq+KUIiVNNIlilZndTXDWAsGF/1WxC0lKsm37DvOnaSn8\nK2ULXRrX4LXbetOlseqHiZRE0SSYu4BngXsBB/5NODWxSLTcnUnzNjDuvUUczshixMCO/PLMVpQr\nG81IeREpjqK5k38bwURfIvmyftch7pmSzGfLd9C7ZW3GX9WdNvVUnFKkpMvtPpjh7v6Ymf2F4Mzl\nR9z97phGJsVeVpbz+ldreGzWUgwYN6gLN/RtoeKUIqVEbmcwi8PfuqFE8mzFtv2MSExm3trdnN2+\nHg9d0ZWmtVWcUqQ0ye0+mOnh79cKLxwp7tIzs5gwZxXPfLicKhXL8uTPT+aKHk1UnFKkFMqti2w6\nx+kay+bul8ckIim2Fm7cy7BJSSzevI9Lujfivsu6UK96xXiHJSJxklsX2RPh7yuBhhydJvk6YGss\ng5Li5XB6Jk9/uJyXPltFnaoV+L8bezGgS8N4hyUicZZbF9mnAGb252Nq00w3M12XEQC+Wb2LkYlJ\nrNpxkGsSmnHPxZ2oWaV8vMMSkSIgmvtgqppZa3dfBWBmrQBNm1zKHTiSwaMzl/D//rOWprUr88bt\nfTmjXd14hyUiRUg0CeZ3wCdmtgowoAVwZ0yjkiLt46XbGD05mc37DnPb6a3444D2VKmg6kEi8mPR\n3Gj5LzNrB3QMm5a4+5HYhiVF0e6DaYx7bxGT52+kXf1qTLrrNHq1qB3vsESkiIpmyuQqwO+BFu7+\nSzNrZ2Yd3P292IcnRYG7MyO1v1K+AAAT7ElEQVR5M2OnpbA3NZ27z23L/5zblorlVJxSRHIWTb/G\nK8A8oF+4vBF4B1CCKQW27jvMmKkL+WDRVro1qckbd/SlU6Ma8Q5LRIqBaBJMG3e/xsyuA3D3Q6a7\n5ko8d+efc9fz4IzFpGVkMeqijtx+hopTikj0okkwaWZWmfCmSzNrA+gaTAm2buchRk1J4osVO+nT\nqg6PXtWdVnU1cFBE8iaaBDMW+BfQzMzeBE4HbollUBIfmVnOq1+u4YlZSylbxnhwcFeu79NcxSlF\nJF9yTTBhV9gSgrv5TyUYpvxbd99RCLFJIVq+dT/DE5OYv24P53asz4ODu9K4VuV4hyUixViuCcbd\n3czed/duwIxCikkKUVpGFi9+upK/fLScahXL8cy1p3D5yY1VnFJEfrJousi+M7Pe7v5tzKORQrVg\n/R5GJCaxZMt+Lju5Mfdd1pmTqqk4pYgUjGgSTF/gF2a2BjhI0E3m7t49loFJ7KSmZfL0h8t46bNV\n1KtekZduSuCCzg3iHZaIlDDRJJgBMY9CCs1/Vu1kZGISa3Ye4ro+zRh1cSdqVFJxShEpeLnNB1MJ\nuAtoCyQDL7t7RrQbNrOJwKXANnfvGraNAwYBWcA24BZ33xQ+1x94GigP7HD3s8P2gcAzQFngb+4+\nPo/vUYD9h9MZP3MJb369juZ1qvD3O/pyWlsVpxSR2DH3488pZmb/ANKBz4CLgLXu/tuoN2x2FnAA\neD0iwdRw933h47uBzu5+l5nVAr4EBrr7OjOr7+7bzKwssAy4ANgAfAtc5+6Lctt3QkKCz52rGQWy\nfbRkK6OnLGTrvsPcfkYrfn9BBypXUJkXEfkxM5t3zPQsP0luXWSdw9FjmNnLwDd52bC7zzGzlse0\n7YtYrMrRGTOvBya7+7pwvW1hex9gRcRUAW8TnAHlmmAksPPAER54bxHTvt9EhwbV+esvenFKs1rx\nDktESoncEkx69gN3zyioYatm9hBwE7AXOCdsbg+UN7NPgOrAM+7+OtAEWB/x8g0Egw6Ot90hwBCA\n5s2bF0isxZW7Mz1pM/e9m8L+w+n87/nt+HX/tlQopzIvIlJ4ckswJ5tZ9hmHAZXD5exRZPmqeOju\no4HRZjYKGEpQKaAc0As4D6gMfGVm/8njdicAEyDoIstPbCXBlr2HuXdqMh8u3sbJzWrx2FXd6dCw\nerzDEpFSKLcpk2PdSf8m8D5BgtkA7HT3g8BBM5sDnBy2N4t4TVOCas5yDHfn7W/X8/CMxaRnZXHv\nJZ249fRWlFWZFxGJk0KdhtDM2rn78nBxEEEZGoBpwHNmVg6oQNAN9lT4fLtwmuaNwLUE12skwtqd\nBxmZmMxXq3bSr/VJjL+qGy1OUnFKEYmvmCUYM3sL6A/UNbMNBGcqF5tZB4JhymsJhkHj7ovN7F9A\nUvjc39x9YbidocAsgmHKE909JVYxFzeZWc7Ez1fz59lLKV+mDOOv7MY1vZupzIuIFAk5DlMuzkrD\nMOWlW/YzfNICFmzYy/md6vPg4G40rFkp3mGJSDFWmMOUpQhKy8ji+Y9X8MInK6hRqTx/ua4Hl3Zv\npLMWESlylGCKke/X72H4pAUs23qAwac05k+XdaFO1QrxDktE5LiUYIqB1LRM/vzBUiZ+sZoGNSox\n8ZYEzu2o4pQiUrQpwRRxX67cwcjEZNbtOsQNfZsz8qKOVFdxShEpBpRgiqi9qemMn7mYt75ZT8uT\nqvD2kFM5tfVJ8Q5LRCRqSjBF0OxFW7l3ajLb9x/hzrNb87vz21OpvIpTikjxogRThOw4cIT73k3h\nvaTNdGxYnZduSqB7UxWnFJHiSQmmCHB3pn2/ifunp3DwSCZ/uKA9d57dRsUpRaRYU4KJs017Url3\n6kI+WrKNHs2D4pTtGqg4pYgUf0owcZKV5fz9m3WMn7mEzCznT5d25ubTWqo4pYiUGEowcbB6x0FG\nJCbxzepdnNG2Lo9c2Y1mdarEOywRkQKlBFOIMjKz+Nvnq3lq9jIqlCvDY1d152cJTVXmRURKJCWY\nQrJo0z5GJCaRvHEvF3ZuwLjBXWlQQ8UpRaTkUoKJsSMZmTz30Qr++slKalUpz/PX9+Tibg111iIi\nJZ4STAzNW7ubEYlJrNh2gCt7NmHMJZ2preKUIlJKKMHEwKG0DB6ftZRXv1xD45qVefXW3vTvUD/e\nYYmIFColmAL2+fIdjJycxIbdqdzUrwXDB3akWkUdZhEpffSXr4DsPZTOQ+8v4p9zN9C6blX+eWc/\n+rSqE++wRETiRgmmAPxr4RbGTFvIroNp/Kp/G357XjsVpxSRUk8J5ifYvj8oTjkjeTOdG9XglVt6\n07VJzXiHJSJSJCjB5IO7M/m7jTzw3iJS0zMZNqADQ85qTfmyKk4pIpJNCSaPNu5J5Z7JyXy6bDu9\nWtTm0au607Z+tXiHJSJS5CjBRCkry3nj67U8OnMJDtx/eRduPLUFZWJcnHLq/I08Pmspm/ak0rhW\nZYYN6MDgHk1iuk8RkYKgBBOFldsPMDIxiW/X7ObMdnV5+IrCKU45df5GRk1OJjU9EwjOnkZNTgZQ\nkhGRIk8JJhfpmVm89Nkqnv5wOZXLl+WJn53MVT2bFFqZl8dnLf0huWRLTc/k8VlLlWBEpMhTgsnB\nwo17GZGYRMqmfVzUtSH3D+pC/eqFW5xy057UPLWLiBQlSjDHOJyeyV8+Ws6Ln66idpUK/PWGnlzU\nrVFcYmlcqzIbj5NMGteqHIdoRETyRgkmwtw1uxiemMSq7Qf5Wa+m3HtJZ2pWKR+3eIYN6PCjazAA\nlcuXZdiADnGLSUQkWkowwMEjQXHK174KilO+flsfzmpfL95h/XCdRaPIRKQ4KvUJ5tNl27lncjKb\n9qZyc7+WDBvQgapFqDjl4B5NlFBEpFgqOn9JC9meQ2mMe28xid9toE29qrxzZz8SWqo4pYhIQSmV\nCWZm8mbGTEth96E0hp7TlqHntlVxShGRAlaqEsy2fYf507QU/pWyha5NavDabb3p0ljFKUVEYqFU\nJBh3Z9K8DYx7bxGHM7IYMbAjvzyzFeVUnFJEJGZKfIJZv+sQ90xJ5rPlO+jTsg7jr+pG63oqTiki\nEmsx+wpvZhPNbJuZLYxoG2dmSWb2vZl9YGaNw/b+ZrY3bP/ezP4U8ZqBZrbUzFaY2cho95+Z5bzy\nxWoGPD2H79buZtygLrw95FQlFxGRQmLuHpsNm50FHABed/euYVsNd98XPr4b6Ozud5lZf+CP7n7p\nMdsoCywDLgA2AN8C17n7otz23e2Unt7+zueYt3Y3Z7evx8NXdqOJ7n4XEcmVmc1z94SC2l7Musjc\nfY6ZtTymbV/EYlXgRNmtD7DC3VcBmNnbwCAg1wSzfOt+ym4/wJM/P5krehRecUoRETmq0K/BmNlD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"]},"metadata":{"tags":[]}}]}]} \ No newline at end of file +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Machine Learning Final Project DTA data.ipynb", + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "code", + "metadata": { + "id": "hsd_fYYqohhi", + "colab_type": "code", + "outputId": "3492c78a-55b9-42d4-9201-1a12f1e3c211", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 + } + }, + "source": [ + "from google.colab import drive\n", + "drive.mount('/content/gdrive')\n", + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "import pandas as pd\n", + "# importing os module \n", + "import os \n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.datasets import make_classification\n", + "\n", + "#Load in data\n", + "os.chdir(\"//content//gdrive//My Drive//PhD casting project//Summary files\")\n", + "\n", + "filename='DTAdatatrimtrain.csv'\n", + "filename1='DTAdatatrimtest.csv'\n", + "\n", + "data = pd.read_csv(filename)\n", + "data.head()\n", + "data.info()\n", + "tdata = pd.read_csv(filename1)\n", + "#F_MC=data['Fraction MC E1']\n", + "\n", + "## Define Data of Interest\n", + "labels=data['Sample ID']\n", + "liquidus=data['Liquidus']\n", + "tliquidus=tdata['Liquidus']\n", + "comp=data.loc[:,'C':'Fe']\n", + "tcomp=tdata.loc[:,'C':'Fe']\n", + "print(comp)\n", + "print(liquidus)" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n", + "\n", + "Enter your authorization code:\n", + "··········\n", + "Mounted at /content/gdrive\n", + "\n", + "RangeIndex: 27 entries, 0 to 26\n", + "Data columns (total 16 columns):\n", + "Sample ID 27 non-null object\n", + "C 27 non-null float64\n", + "Cr 27 non-null float64\n", + "Ni 27 non-null float64\n", + "Mn 27 non-null float64\n", + "Si 27 non-null float64\n", + "Mo 27 non-null float64\n", + "Nb 27 non-null float64\n", + "W 27 non-null float64\n", + "Ti 27 non-null float64\n", + "Zr 27 non-null float64\n", + "Fe 27 non-null float64\n", + "Liquidus 27 non-null float64\n", + "Te1 27 non-null object\n", + "Te2 27 non-null float64\n", + "STR 27 non-null float64\n", + "dtypes: float64(14), object(2)\n", + "memory usage: 3.5+ KB\n", + " C Cr Ni Mn Si ... Nb W Ti Zr Fe\n", + "0 0.485 23.245 33.761 0.972 0.765 ... 0.393 0.1050 0.0660 0.00 39.6340\n", + "1 0.440 23.632 34.191 0.913 0.773 ... 0.380 0.1380 0.0670 0.00 38.9870\n", + "2 0.450 26.732 34.639 0.829 1.503 ... 1.036 0.0000 0.0770 0.00 34.6500\n", + "3 0.410 25.940 35.640 0.960 2.000 ... 1.110 0.0900 0.0300 0.12 33.1500\n", + "4 0.410 25.690 36.040 0.900 1.900 ... 1.110 0.0900 0.0400 0.14 33.1100\n", + "5 0.420 25.390 35.710 0.950 2.000 ... 1.110 0.0800 0.0400 0.10 33.5900\n", + "6 0.430 25.000 35.000 0.600 1.300 ... 0.800 0.1260 0.1300 0.19 36.0475\n", + "7 0.430 25.000 34.000 0.600 1.300 ... 1.110 0.0910 0.1300 0.17 36.8251\n", + "8 0.450 26.000 35.000 0.700 1.300 ... 0.800 0.1130 0.1300 0.18 35.6654\n", + "9 0.440 24.930 34.000 1.260 1.380 ... 1.300 0.0000 0.0000 0.00 36.6100\n", + "10 0.326 24.700 34.400 0.912 1.120 ... 0.404 0.0878 0.0057 0.00 37.7624\n", + "11 0.523 25.200 35.000 0.899 1.140 ... 0.418 0.0879 0.0064 0.00 36.4539\n", + "12 0.343 24.290 34.420 0.945 1.210 ... 0.994 0.0425 0.0065 0.00 37.5269\n", + "13 0.514 25.800 34.700 0.932 1.160 ... 1.150 0.0387 0.0074 0.00 35.4800\n", + "14 0.336 25.200 34.600 0.901 1.140 ... 1.470 0.0305 0.0081 0.00 36.0724\n", + "15 0.554 25.500 34.500 0.942 1.150 ... 1.480 0.0336 0.0087 0.00 35.6089\n", + "16 0.499 25.000 35.400 1.120 0.807 ... 0.978 0.0181 0.0081 0.00 35.9586\n", + "17 0.425 25.500 34.900 0.848 1.310 ... 0.922 0.0379 0.0080 0.00 35.8201\n", + "18 0.445 25.400 34.100 0.933 2.570 ... 1.010 0.0685 0.0095 0.00 35.1628\n", + "19 0.407 24.800 34.100 0.923 1.120 ... 1.610 0.0317 0.1140 0.00 36.6350\n", + "20 0.430 25.000 34.000 0.790 1.200 ... 0.910 1.5900 0.1380 0.00 35.9922\n", + "21 0.448 24.800 34.700 0.853 1.050 ... 1.440 1.4500 0.0195 0.00 34.9732\n", + "22 0.450 25.000 34.000 0.940 1.200 ... 0.960 0.0910 0.1300 0.00 37.3938\n", + "23 0.430 25.000 34.000 0.990 1.210 ... 1.500 1.5400 0.1560 0.00 35.4232\n", + "24 0.424 25.200 34.000 0.892 1.220 ... 1.450 0.0628 0.0091 0.00 36.4819\n", + "25 0.452 24.900 34.400 0.900 1.120 ... 0.944 0.0342 0.0084 0.00 36.9844\n", + "26 0.430 25.300 34.700 0.976 1.150 ... 0.506 0.0638 0.0070 0.00 36.5904\n", + "\n", + "[27 rows x 11 columns]\n", + "0 1382.850000\n", + "1 1382.400000\n", + "2 1362.550000\n", + "3 1351.600000\n", + "4 1352.150000\n", + "5 1352.350000\n", + "6 1367.550000\n", + "7 1370.400000\n", + "8 1370.650000\n", + "9 1359.900000\n", + "10 1383.300000\n", + "11 1368.400000\n", + "12 1375.400000\n", + "13 1361.500000\n", + "14 1366.000000\n", + "15 1356.700000\n", + "16 1371.900000\n", + "17 1365.200000\n", + "18 1348.850000\n", + "19 1364.050000\n", + "20 1366.150000\n", + "21 1361.766667\n", + "22 1369.600000\n", + "23 1357.050000\n", + "24 1367.800000\n", + "25 1368.200000\n", + "26 1374.800000\n", + "Name: Liquidus, dtype: float64\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cHXFN-UaWdOm", + "colab_type": "text" + }, + "source": [ + "some dumb line" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "tbBL8aI11Oll", + "colab_type": "code", + "colab": {} + }, + "source": [ + "# %% Visualize Data\n", + "plt.figure(figsize=(10,6))\n", + "plt.scatter(data['C'],data['Liquidus'])\n", + "plt.xlabel('Concentration (wt.%)')\n", + "plt.ylabel('Temperature (C)')\n", + "plt.show()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "7slpzDth25jB", + "colab_type": "code", + "outputId": "e2537815-fdac-4eb6-d12e-cd2a9ba41e9d", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 422 + } + }, + "source": [ + " # Trees!\n", + "\n", + "from sklearn.ensemble import RandomForestRegressor\n", + "from sklearn.datasets import make_regression\n", + "\n", + "X=comp\n", + "y=liquidus\n", + "#X, y = make_regression(n_features=4, n_informative=2,\n", + "# random_state=0, shuffle=False)\n", + "\n", + "regr = RandomForestRegressor(max_depth=20, random_state=0,\n", + " n_estimators=100)\n", + "\n", + "regr.fit(X, y) \n", + "\n", + "print(regr.feature_importances_)\n", + "\n", + "print(regr.predict(tcomp))\n", + "print(tliquidus)\n", + "\n", + "plt.scatter(tliquidus,regr.predict(tcomp))\n", + "plt.plot([1350,1400],[1350,1400])\n", + "plt.ylim(1350,1400)\n", + "plt.xlim(1350,1400)\n", + "plt.xlabel('Measured Temperature (C)')\n", + "plt.ylabel('Predicted Temperature (C)')" + ], + "execution_count": 0, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[0.01670288 0.08567572 0.00881047 0.03942633 0.13336387 0.02787641\n", + " 0.18501999 0.01289004 0.00702579 0.00138209 0.4818264 ]\n", + "[1368.957 1352.84250002 1367.56650001]\n", + "0 1362.80\n", + "1 1357.85\n", + "2 1363.90\n", + "Name: Liquidus, dtype: float64\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Text(0, 0.5, 'Predicted Temperature (C)')" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 3 + }, + { + "output_type": "display_data", + "data": { + "image/png": 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