Lenselink_4_Clustering_embeddings.ipynb

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      "/home/verena/anaconda3/envs/wonderland/lib/python3.6/site-packages/umap/spectral.py:4: NumbaDeprecationWarning:\n",
      "\n",
      "No direct replacement for 'numba.targets' available. Visit https://gitter.im/numba/numba-dev to request help. Thanks!\n",
      "\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly.express as px\n",
    "import umap\n",
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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       "     Compound_ID  bit_1  bit_2  bit_3  bit_4  bit_5  bit_6  bit_7  bit_8  \\\n",
       "0  CHEMBL2364776      1      0      0      1      1      1      1      1   \n",
       "1  CHEMBL2365129      1      0      0      1      1      1      1      1   \n",
       "2    CHEMBL29641      1      1      1      0      0      0      0      0   \n",
       "3  CHEMBL1229592      1      1      1      1      1      0      0      1   \n",
       "4   CHEMBL360190      0      0      1      0      0      0      0      0   \n",
       "\n",
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       "2      0  ...        0        0        1        0        0        0        0   \n",
       "3      1  ...        0        0        1        0        0        0        1   \n",
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       "\n",
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       "3        0        0        1  \n",
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       "\n",
       "[5 rows x 257 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "emb = pd.read_table('../datasets/Lenselink_et_al/Dataset_files_342_MB/data/Supplementary_Information/dataset/compound_features_256.txt')\n",
    "emb.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "'''\n",
    "umap_input = np.zeros([len(emb), 257], dtype=np.int8)\n",
    "\n",
    "for i in range(len(df)):\n",
    "    umap_input[i] = np.asarray(list(df.iloc[i,17]),\n",
    "                               dtype=np.int8)\n",
    "'''\n",
    "\n",
    "emb = emb.drop(columns=['Compound_ID'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "def umap_reduce(embeddings, **kwargs):\n",
    "    umap_params = dict()\n",
    "\n",
    "    umap_params['n_components'] = kwargs.get('n_components', 2)   # dimensionality of the output\n",
    "    umap_params['random_state'] = kwargs.get('random_state', 420)\n",
    "    umap_params['verbose'] = kwargs.get('verbose', 1)\n",
    "    \n",
    "    # ADAPT:\n",
    "    umap_params['min_dist'] = kwargs.get('min_dist', .8)\n",
    "    umap_params['n_neighbors'] = kwargs.get('n_neighbors', 5)\n",
    "    umap_params['metric'] = kwargs.get('metric', 'cosine')\n",
    "    \n",
    "    transformed_embeddings = umap.UMAP(**umap_params).fit_transform(embeddings)\n",
    "\n",
    "    return transformed_embeddings"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Somehow, none of the metrics for binary data I've used finished training. :(\\\n",
    "Cosine won't finish either.."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "UMAP(a=None, angular_rp_forest=True, b=None,\n",
      "     force_approximation_algorithm=False, init='spectral', learning_rate=1.0,\n",
      "     local_connectivity=1.0, low_memory=False, metric='cosine',\n",
      "     metric_kwds=None, min_dist=0.8, n_components=2, n_epochs=None,\n",
      "     n_neighbors=5, negative_sample_rate=5, output_metric='euclidean',\n",
      "     output_metric_kwds=None, random_state=420, repulsion_strength=1.0,\n",
      "     set_op_mix_ratio=1.0, spread=1.0, target_metric='categorical',\n",
      "     target_metric_kwds=None, target_n_neighbors=-1, target_weight=0.5,\n",
      "     transform_queue_size=4.0, transform_seed=42, unique=False, verbose=1)\n",
      "Construct fuzzy simplicial set\n",
      "Mon Jul  6 11:47:03 2020 Finding Nearest Neighbors\n",
      "Mon Jul  6 11:47:03 2020 Building RP forest with 33 trees\n"
     ]
    }
   ],
   "source": [
    "#transformed_embeddings = umap_reduce(emb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas import DataFrame\n",
    "\n",
    "def render_2D_scatter_plotly(embeddings_dataframe: DataFrame, title=''):\n",
    "    fig = px.scatter(embeddings_dataframe,\n",
    "                     template='ggplot2',\n",
    "                     x='x',\n",
    "                     y='y',\n",
    "                     color='label',\n",
    "                     hover_name=embeddings_dataframe.index,\n",
    "                     title=title,\n",
    "                     width=600,\n",
    "                     height=600,\n",
    "                     color_continuous_scale='Bluered_r'                   \n",
    "                    )\n",
    "\n",
    "    fig.layout.coloraxis.showscale=False\n",
    "    fig.update_xaxes(showticklabels=False)\n",
    "    fig.update_yaxes(showticklabels=False)\n",
    "    \n",
    "    return fig"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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