Mangaki on Earth (MOE).ipynb

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    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import offsetbox\n",
    "from sklearn import (manifold, datasets, decomposition, ensemble,\n",
    "                     discriminant_analysis, random_projection)\n",
    "from MulticoreTSNE import MulticoreTSNE as TSNE\n",
    "from sklearn.preprocessing import StandardScaler"
   ]
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   "source": [
    "from mangaki.utils.data import Dataset\n",
    "from mangaki.utils.values import rating_values\n",
    "dataset = Dataset()\n",
    "triplets = Rating.objects.values_list('user_id', 'work_id', 'choice')\n",
    "dataset.make_anonymous_data(triplets)\n",
    "dataset.save('ratings.pkl')\n",
    "dataset.load('ratings.pkl')"
   ]
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     "text": [
      "(2071, 10086)\n"
     ]
    }
   ],
   "source": [
    "import scipy.sparse as sp\n",
    "scaler = StandardScaler()\n",
    "X = scaler.fit_transform(\n",
    "    sp.coo_matrix((dataset.anonymized.y, (dataset.anonymized.X[:, 0], dataset.anonymized.X[:, 1]))).toarray().astype(np.float64)\n",
    ")\n",
    "print(X.shape)"
   ]
  },
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     "output_type": "stream",
     "text": [
      "(10086, 2071)\n"
     ]
    }
   ],
   "source": [
    "M = X.T\n",
    "print(M.shape)"
   ]
  },
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   "source": [
    "model = TSNE(n_components=2, init='pca', verbose=2, n_jobs=4)\n",
    "X_2d = model.fit_transform(M)"
   ]
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   "source": [
    "mds = manifold.MDS()\n",
    "X_2d_mds = mds.fit_transform(M)"
   ]
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       "        window._bokeh_failed_load = true;\n",
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       "        var cell = $(document.getElementById(\"11d40564-3008-462f-b8ee-daa45c38ed55\")).parents('.cell').data().cell;\n",
       "        cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
       "      }\n",
       "  \n",
       "    }\n",
       "  \n",
       "    if (window._bokeh_is_loading === 0) {\n",
       "      console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
       "      run_inline_js();\n",
       "    } else {\n",
       "      load_libs(js_urls, function() {\n",
       "        console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
       "        run_inline_js();\n",
       "      });\n",
       "    }\n",
       "  }(this));\n",
       "</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from bokeh.plotting import figure, show, output_notebook\n",
    "from bokeh.models import ColumnDataSource,LabelSet\n",
    "\n",
    "class MangakiViz:\n",
    "    def __init__(self, titles, coords, drawn):\n",
    "        work_ids = dataset.encode_works(drawn)\n",
    "        self.source = ColumnDataSource(\n",
    "            data={\n",
    "                'x': coords[work_ids, 0],\n",
    "                'y': coords[work_ids, 1],\n",
    "                'titles': [titles[work_id] for work_id in drawn]\n",
    "            }\n",
    "        )\n",
    "        self.p = figure(\n",
    "            title='MDS embedding of Mangaki',\n",
    "        )\n",
    "        self.p.scatter(x='x', y='y', size=8, source=self.source)\n",
    "        self.labels = LabelSet(x='x', y='y', text='titles', level='glyph', x_offset=5, y_offset=5, source=self.source,\n",
    "                              render_mode='canvas')\n",
    "        self.p.add_layout(self.labels)\n",
    "        show(self.p)\n",
    "\n",
    "output_notebook()\n",
    "titles = dict(Work.objects.values_list('id', 'title'))\n",
    "popular = list(Work.objects.popular()[:200].values_list('id', flat=True))\n",
    "sampled_items = []\n",
    "viz = MangakiViz(titles, X_2d_mds, popular)"
   ]
  }
 ],
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