From 2fdd486fb52112495ac8816561cfeb4067c2f6c7 Mon Sep 17 00:00:00 2001 From: w-bonelli Date: Wed, 16 Feb 2022 08:47:47 -0500 Subject: [PATCH] debug KNN, add KNN section to examples notebook --- cactice/knn.py | 56 +++++-- cactice/rns.py | 10 +- notebooks/explore.ipynb | 355 ++++++++++++++++++++++++++++++++++++---- 3 files changed, 374 insertions(+), 47 deletions(-) diff --git a/cactice/knn.py b/cactice/knn.py index 5382aad..7a12774 100644 --- a/cactice/knn.py +++ b/cactice/knn.py @@ -1,10 +1,10 @@ from typing import List, Dict, Tuple from itertools import islice from collections import Counter +import logging import numpy as np -# TODO: more distance function options? from cactice.neighbors import get_neighborhood, Neighbors from cactice.distance import hamming_distance import cactice.stats as stats @@ -22,6 +22,8 @@ def __init__( :param neighbors: Which adjacent cells to consider neighbors. :param layers: How many layers of adjacent cells to consider neighbors. """ + + self.__logger = logging.getLogger(__name__) self.__k: int = k self.__neighbors: Neighbors = neighbors self.__layers: int = layers @@ -66,26 +68,56 @@ def predict(self, grids: List[np.ndarray] = None) -> List[np.ndarray]: # find cells to predict (missing locations) rows = range(0, grid.shape[0]) cols = range(0, grid.shape[1]) - missing = [(i, j) for i in rows for j in cols if grid[i, j] == 0] + grid_pred = grid_predictions[gi].copy() + missing = [(i, j) for i in rows for j in cols if grid_pred[i, j] == 0] + + # if this grid has no missing locations, skip it + if len(missing) == 0: continue # predict cells one by one for i, j in missing: # get the missing location's neighbors - neighborhood = get_neighborhood(grid, i, j, self.__neighbors, self.__layers) + neighborhood = get_neighborhood( + grid=grid, + i=i, + j=j, + neighbors=self.__neighbors, + layers=self.__layers, + exclude_zero=True) + + # ignore central cell + del neighborhood[(0, 0)] + + # pull out neighbor cell values + neighbors = list(neighborhood.values()) - # compute distance from this neighborhood to every training neighborhood - distances = {nh[(0, 0)]: hamming_distance(list(neighborhood.values()), list(nh.values())) for nh in neighborhoods} + if len(neighbors) > 0: + self.__logger.debug(f"Assigning location ({i}, {j}) via KNN") - # sort distances ascending - distances = dict(sorted(distances.items(), key=lambda k, v: v, reverse=True)) + # compute distance from this neighborhood to every training neighborhood + distances = {nh[(0, 0)]: hamming_distance(list(neighborhood.values()), list(nh.values())) for nh in neighborhoods} - # keep k most similar neighborhoods (k nearest neighbor neighborhoods) - distances = dict(islice(distances, self.__k)) + # sort distances ascending + distances = dict(sorted(distances.items(), key=lambda k, v: v, reverse=True)) - # count frequency of each cell value in and pick the most common (ties broken randomly) - cell_prediction = Counter(distances.values()).most_common(1)[0][0] + # keep k most similar neighborhoods (k nearest neighbor neighborhoods) + distances = dict(islice(distances, self.__k)) + + # count frequency of each cell value in and pick the most common (ties broken randomly) + cell_pred = Counter(distances.values()).most_common(1)[0][0] + else: + self.__logger.debug( + f"Location ({i}, {j}) has no neighbors, assigning by sampling from cell distribution") + + # sample randomly according to cell class distribution + cell_pred = np.random.choice( + a=list(self.__cell_distribution.keys()), + p=list(self.__cell_distribution.values())) # set the cell in the corresponding grid - grid_predictions[gi][i, j] = cell_prediction + grid_pred[i, j] = cell_pred + + # set the predicted grid + grid_predictions[gi] = grid_pred return grid_predictions diff --git a/cactice/rns.py b/cactice/rns.py index 5cf38c9..35848ee 100644 --- a/cactice/rns.py +++ b/cactice/rns.py @@ -22,11 +22,12 @@ def __init__( :param neighbors: The cells to consider part of the neighborhood. :param layers: The width of the neighborhood """ + + self.__logger = logging.getLogger(__name__) self.__neighbors: Neighbors = neighbors self.__layers: int = layers self.__train: List[np.ndarray] = [] self.__cell_distribution: Dict[str, float] = {} - self.__logger = logging.getLogger(__name__) def fit(self, grids: List[np.ndarray] = None): """ @@ -78,10 +79,13 @@ def predict(self, grids: List[np.ndarray] = None) -> List[np.ndarray]: # predict cell value by making a random selection from its neighbors, if any # or if none, choosing randomly according to the observed class distribution if len(neighbors) > 0: - self.__logger.debug(f"Assigning location ({i}, {j}) from neighbors") + self.__logger.debug(f"Assigning location ({i}, {j}) via RNS") cell_pred = random.choice(neighbors) else: - self.__logger.debug(f"Location ({i}, {j}) has no neighbors, sampling from distribution") + self.__logger.debug( + f"Location ({i}, {j}) has no neighbors, assigning by sampling from cell distribution") + + # sample randomly according to cell class distribution cell_pred = np.random.choice( a=list(self.__cell_distribution.keys()), p=list(self.__cell_distribution.values())) diff --git a/notebooks/explore.ipynb b/notebooks/explore.ipynb index 52b493b..dde956a 100644 --- a/notebooks/explore.ipynb +++ b/notebooks/explore.ipynb @@ -558,6 +558,13 @@ }, "output_type": "display_data" }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, { "name": "stdout", "output_type": "stream", @@ -580,13 +587,6 @@ " (4, 4): 0.03125}\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" - ] - }, { "data": { "text/plain": "
", @@ -935,18 +935,18 @@ "output_type": "display_data" }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Grid 'from_text' undirected bonds (vertical):\n", - "{(0, 0): 0.687, (0, 1): 0.254, (1, 1): 0.06}\n" + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + "Grid 'from_text' undirected bonds (vertical):\n", + "{(0, 0): 0.687, (0, 1): 0.254, (1, 1): 0.06}\n" ] }, { @@ -960,18 +960,18 @@ "output_type": "display_data" }, { - "name": "stdout", + "name": "stderr", "output_type": "stream", "text": [ - "Grid 'from_text' undirected bonds (combined):\n", - "{(0, 0): 0.687, (0, 1): 0.254, (1, 1): 0.06}\n" + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" ] }, { - "name": "stderr", + "name": "stdout", "output_type": "stream", "text": [ - "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + "Grid 'from_text' undirected bonds (combined):\n", + "{(0, 0): 0.687, (0, 1): 0.254, (1, 1): 0.06}\n" ] }, { @@ -1108,12 +1108,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 15, "outputs": [ { "data": { "text/plain": "
", - "image/png": 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" }, "metadata": { "needs_background": "light" @@ -1155,6 +1195,8 @@ "for name, grid in grids.items():\n", " if name == 'from_text': continue # skip the binary tile grid\n", " imputed = rns.predict([grid])[0]\n", + "\n", + " plot_grid(grid, f\"Grid {name} original\")\n", " plot_grid(imputed, f\"Grid {name} imputed (RNS)\")" ], "metadata": { @@ -1192,7 +1234,7 @@ { "data": { "text/plain": "
", - "image/png": 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\n" + "image/png": 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\n" 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\n" 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\n" 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\n" + "image/png": 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\n" }, "metadata": { "needs_background": "light" @@ -1283,7 +1325,7 @@ "source": [ "### K-nearest neighbors\n", "\n", - "TODO" + "The k-nearest neighbors model attempts to find cell neighborhoods similar to the location to be predicted, ranks them according to similarity, and selects the most common central cell value from a subset of the most similar neighborhoods (ties are broken randomly)." ], "metadata": { "collapsed": false @@ -1291,9 +1333,258 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "outputs": [], - "source": [], + "source": [ + "from cactice.knn import KNN\n", + "from cactice.neighbors import Neighbors\n", + "\n", + "knn = KNN(k=10, neighbors=Neighbors.CARDINAL, layers=1,)" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Fit the model on the grids we've loaded:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 11, + "outputs": [], + "source": [ + "knn.fit(list(grids.values()))" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "Now impute missing locations on the multinomial training grids:" + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 16, + "outputs": [ + { + "data": { + "text/plain": "
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\n" 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\n" 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7KkQ2yWj7OEmVkroCZ5GM3ELpY2bL0+sraOWcskXqa8CTocIkXS/pfWAccHXA3FHAUjObU0hOsRTuzJLUDZgKXLHVO3W7mdlnZjaUZARxlKSDCs2UdA6wysxeLTSrBcea2WHAmcBlko4PkFlG8lHzdjM7FNhI8jE+CEnlwEjg94HyepOMXAcCVUCFpH8OkW1mC0imA54C/gzMBj4Lkb2N1zICfcrbESRdBTQC94fKNLOrzKw6zfxWiMz0DfeHBHgjKJbCvZTPjx76p/cVNUm7kRTt+83skdD56bTAX4ARAeKOAUZKWkwyFXWypPsC5AL/P9rEzFYBj5JMfxXqA+CDnE8cD5MU8lDOBF4zs1BnNj4VeNfMVpvZZuAR4OhA2ZjZ3WZ2uJkdT3IC79pQ2cBKSX0B0p+rAmZHI+ki4BxgXPqGE9r9wHmBsgaRvKnPSbfD/sBrkvba3qBiKdyvAPtKGpiOgsYCj+/kNrVKkkjmXheY2c8D5n6h+dtxSV2A04C/FZprZleaWX8zqyHp3+fMLMhoUFKFpO7N10m+NCp4Lx4zWwG8L2lIetcpwJuF5ub4JwJNk6TeA4ZJ6pquH6eQfPcRhKQ9058DSOa3HwiVTbK9XZhevxB4LGB2FJJGkEz9jTSzhoC5++bcHEWA7Q/AzOaZ2Z5mVpNuhx+Q7OCwoj1hRXEhmbOrJdm75KqAuQ+SzDduTjvqkkC5x5J8nJxL8rF1NnBWgNwvAq+nufOBqyP09YkE3KuEZG+gOenljcB/v6HArLQ//hvoHSi3AqgDegbu20kkG/p84LdAp4DZ/0PyxjUHOKWAnL/bJoBK4FlgIckeK7sHzB6dXv8EWAlMC5S7iOS7sebtb7v3/mghd2r695sL/AHoF6ovtnp8Me3cq8T/c9I55zKmWKZKnHPOtZEXbuecyxgv3M45lzFeuJ1zLmO8cDvnXMZ44XbOuYzxwu2ccxnjhds55zLm/wCAYeokdgKn0wAAAABJRU5ErkJggg==\n" 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\n" 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\n" 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Qsrg3SWhbvMTvXoL5VNvftPh/opk9N8rrGwAbm1nlLcvRlsRVuCmdAklZ6eSVYd+DYVI6eYv/XzJYy9cfBpq7TZfJZEYlSxMzmQGicaVTTBb/mMz3HsJ0r+VtqiL70GUn+MXQhvrxCkoZ6693eYbNZAaIoc387yXmTrUtMcEPHiL7Fqnt4hDb9547P8AQZ/73EnOn2paY4AcPkX2L1HZxiO17z50fYIgz/3uJuVNtS+wOA02L7CHNXRxihfxeOz+0CJ5hJZ1jZu+O9oi/+L1JMfcgtCUED5F96rs4pByUUiX+v2TE41JgVuv/LnZJZP6HZsXcg9CWUJoW2ae+i0PqQSlVM+ymwG3A6YBRJA/fBTilm1Eqmf+bFnOn3pZeaEpkn/IuDoMQlFI1YHcBPgIcC/yTmc2T9IyZ/TTWoZf43UPMnXJbYvAQ2ae6i8OgBKVUSRNXAl+U9J3y75Iqm1TwEnN74BUw4SGyj8EjyCK27713fugq/n9JYekA4A1m9sm6Nl6Z/z3UQTGqrRi8VDihwQwpK51CVWgpp9+dPnlmnPh/JGb2PWD0FBKZTKZxBmJ52xQxoU+pEhMuFjNjehCj2Y6ZlUPxCsmbPnlmx2NZS5zJDBB5wGYyA0QW/7fhITL3akvKOyxA8+cstC9T3vmgnSz+b8NDZO7VlpR3WIDmz1loX6a888Eq9rVKjTGpCuaheZF5jI1XIIPXbgke5wzC+jL1nQ9aBM2wkt4I7ArMN7MrQmzbSVkw7yEyTzWQwQuvcxYbyJDizgctqsT/17U9/wBF5v91gOMkHdPFbmDF/02LzFMNZPDC85zFBDKkuvNBi6oZdo2250cBe5vZQ2Xm/18BnxvNaJDF/y2aEpmnGsjgRT92PqgbyDAIOx9UDdgJktalmIllZg8BmNlTkl4I8lSSsmDeQ2SeaiCDF17nLLQvU975oJ2qATsFuJEirM4kbWxmiyRNLl9Lln5kZW8Kr0AGrx0WPAjty5R3PmgnSPz/opE0CdjQzP5QVTZl8X+oNHHY0px6iNljxP8xhEoTt5le72eUfrDvZnuPjfi/RbnXTuVgzWQyY0vUDBvCgifvCXbg9amcKh7hZRC+H09Mv8Ts+ROzh5FHIIPXdXn8uz/WcYbNWuJMZoDoizTRS3/atF7Vy8ZL4+zRLyknOE9VF91OX2bYfQ/ah5NOG3Xz9q60tKRNlU/VpqWLnTPvMubMu5QZ605jo8kbjHm9PPolxkdM+2P8eF2XsTbQpwHroT/10qumqIuN9eHRLyknOE9ZF91iYDJOhGpJvfSqqepie9Erh+DlxyPBeQzeWvIqLfHrJL28fL6WpBMkXSpptqQpXexe1BKfd+ac4EqNJFRL6qVXTVUX24teOQQvP9B8gvMYvLXkUD3DnglsXz4/FXgamA3sBZwFzBrNqF1LHPOzzkhCtaReetVUdbG9+qiLl592mkpwHkM/+r9SS2xmLc3wLma2U/n8Z5Lm1fIwBoRqSb30qqnqYmN8xODlxyPBeQxe/d9O1YCdL+m9ZnYWcJOkXczsBklbActrexlBqvrTVPHSOHv0S8oJzgfhuuyqdCq/p54KvAl4GNgJWFA+PmxmlT8GZqVTOFnplJVOnY5VbdXxGHBkeeNp87L8/Wa2ZGyrmMlk6lDrZx0zexyoltZkMplGSfJ32JhlVAypLr2jlnfbVxfplZiwv1SX0V6hkmNNFv9nMgPEQIj/h1HI3bRg3sOml6TYKQYMhNbL2wYGRPw/jELupgXzHjatpNjHnvEpPvqlj3PtJdew6N56WTxSDBgIrZe3DQyI+H/YhNwegnkPm9ik2KkGDKTa/+1UaYk/LGl68LsOKC1RdkgWDi+b1AlJiu3VfiEO3/5APrDrody3bFHtgImU+79qhv0sMFfStZKOllTrF/2xFv97kLr4P2VCkmKnGjAwKP1fddPpbmBn4C+AdwInSLoR+BZwoZk9MZrRWIv/PRgU8X9qhCbFTjVgYFD6v2rAmpmtBK4ArpC0BrA/8C7g34Hmt712ImXxf6rEJMVONWBgUPq/asCuomk0s+XAJcAlZW7iKEJF1sMq5K6LV/tDbXpNil0Xr4CBQaBK/L+Vmf2uFwcpL4lTVTp5Kb1Cue3RO4Ntfnn1zcE2413pFJ3mtNfBmslkxpYsTcxkBojGpYkxy6gYPPaJiRG/x9QrZqkeE0Mbur9M1DneM9wkhtAlbkxfxizvx3qfpDzDZjIDhLv4P0YwHmPjIf73rFdo3ULF77FCfo+AgVT70tsP9GHAtgTj07eczrNPP8vJR89m6523YeMZnZdnMTb7HrQPbzv0YGYfd3JQ/Vqi7Do5Yz3rFVq3lvh9+coXmCDxjm33456lC1ncQZ4X05aY9gxTX3r7gT4siWME4zE2HuJ/r3rF1A3CxO+xQn6PgIFU+9LTT4uuM6ykNYHDgAfM7EeSDgd2B24Hvl4KKaIJEYz3YlOXXjKyN1kv8NktoEXTbfHw49WX3tdM1Qx7FnAA8BFJ5wLvAOYCfwac3smoXfx/+ZzvjVomRDDei01dehFlN1mvXuoWky2/6bZ4+PHqy35cM1XfYbc1s+0krQ4sBKaZ2QpJ36RLUrZ28f8P77vyJSuxUMF4rE0IsaLspuvVS91a1M2W79EWDz9efdmPa6Yy83+5LF4bmARMAR4FJgJrBHkqiRGMx9iEEiPK9qhXbN1Cxe9ebRmmvuzHNVM1YM8A7gBWA44FviPpbmA34NvB3ogTjMfYeIj/U60XhIvfY4X8HgEDqfZlP/x0Ff8DSJoGYGYPSJpKERt7n5ldV8fBaEviJvBQFGWlU3hbYpRuqfall9Jp3832jsv8D8VAbXu+DLgguAaZTGZMSDKReKrcsaBeRsB2YmYLr5k8VWJm5dBzFnO+fkn4DDvW/ZK1xJnMAJEHbCYzQGTx/wiaFNjH1sur/anuFjBM10wvwR+Qxf8voUmBfWy9vNrvYePV/6leM70Ef0AW/69C0wL72Hp5tT/V3QKG6ZqJDf5oUTnDSnoVMAuYDqwAfgfMKfeM7YlhEP/HCuxj8RLme+DV/6ldM71QuVUH8FXgZRSC/4kUA/dXkvbsYjduxP8xAvtYvIT5Hnj1f4rXTC9UzbAfAHYoBf9fAC43sz0lfQ24GNhxNKPxJP5vUVdgH4uXMN8Dr/5P/ZqJoc5Np9UplsITgckAZnZfuQtAMMMk/g8V2MfiJcz3wKv/U71meqVqwJ4OXC9pLvAmYDZAuSnWozEOh0n8H5NdPqZeXu1PdbeAYbpmevVRR/z/WuBPgPlmdkdoBYdJ/B8jsJ+1/R7BNl6CeQ+80tyG4hWUEbOLwfTJM3sS/98K3BrsNZPJjDmVM2yveM2wMYSGSw2bKD90xkh1zx8In8ljQuW8iN5bJ5PJpEUesJnMADG04v/YDOspZv73soGw9sf48WqLR/9720AfZtiWKPvYMz7FR7/0ca695BoW3ds9MNzLBv4o5G6qLfsetA8nnXZirff3toGw9sf48WqLR/9720C1NHGKpM9JukPSo5IekXR7+drUYG+kLf5ONfO/l01M8EOoH6+2ePS/p02Lqhn2fGApsKeZrWdm6wNvLl87P9jbCFITf7eE3DF3zodBlN9L+1Omyf73smlRNWBnmtlsM1vcesHMFpvZbGBGJ6OUxP91bVLO/O9BP4TsHjTZ/1427VTddLpX0ieAb5jZEgBJGwJHAgs6GaUi/g+xSTnzvwf9ELI3TdP972XTTtWAfSdwDPBTSa8sX1sCXEKxz04wqYq/U87870E/hOxN4tH/XjbtdB2wZrYU+OfysQqS3kuxWVYQKYu/PdqSqig/llA/Xm3xyuTvTbQ0UdJ9ZrZZVbksTczSRA/GizSxan/YTq0SsGEvlcpkMuFUfYfdENiX4mecdgT8oo6DmGz5MSFpMaFPoTNmTFtiiPn0j5n9Up4xQwldycRk8ffYv6iKqgF7GTDZzOaNPCDp6jGtSSaTqaTqptP7uxw7PMahV/JtCNdrhupPY9oSq3ENbU+qmuVU69Wi6UTyvfQ/9EFL3Eq+PWfeZcyZdykz1p3GRpM36GrjpYsN1Z/GtCVW4xranlQ1y6nWq0XIOfbuf+hTeJ1H8u0YvWaM/jS0LTE+wCdhtYdNqvUCn0Tysf3fIjq8TtL3zWz/KFuH5Nu9Jniuqz/tpS0h+mPvhNXjEe9E8jH686ponZ06PHYGduhi96KW+BcXX/WS400n3+5VrxmiDY5tS4iPYdX5poR3IvlY/XnVDHs98FOKn3FG0rFm7VriU39+TsdVQlPJt3vRa8Zqg0PaEupjGHW+qeGZSL4X/XnVgL0d+KCZvURGIqmj+L8bHsm3Y/WaofrTmLbE6I+HTeebIl6J5HvVn1cN2OPpvGz++2Bv+CXfjiFUfxrTFi+Na6qa5VTrFUM/+r8XLfF7zaxS/N9tSdyJYVI6xShdvJRO45mY68VL6bTvZns3kub0hB5sM5lMBFn8n8kMEI2L/5f+/qHgSt02PXw/lpglYWhI1nhf3nrtk+MRMBKzvI1hrMMrs/g/kxkg3MX/LVJM2B3qZ7wL+b0SvHsEjMT48AxkaeGe+b9FS2RdRwbWEkxP33I6zz79LCcfPZutd96GjWd0XqLue9A+vO3Qg5l93Mm16xTqJ6ZeLULaH9MWD5uY9sfYtET2y1e+wASJd2y7H/csXcjiLjLA0LbE+PCo10iqpIkvl3SSpHMlHT7i2JejPJJuwu5QP+NdyO+V4B18AkZCfXjVq52qGfYs4E7gu8D7JP0VcLiZPQfsFuu0FyG7V8LuUD/jXcjfdFJ4j4CRGB8e9Wqn6nfYLczsGDO7yMwOBn4N/ETS+t2M2sX/N171q1WODULC7lA/413I75EUvumAkVgfHvVqp2qGnShpgpmtBDCzEyUtBK4BJncyahf/H3/OKausElJP2B3qZ7wL+b2SwrdoKmCkVx8e9YLqAXsp8BbgR60XzOxsSYuB02IcppywO9TPeBfyeyWF9wgYifHhUa+RVP2s84kOr/9A0r81U6WX4pWwO9TPeBfyeyV49wgYifHRj0CWxhOJj1wS18ErYbeHcicrncLxUDpdeNM1wT5iiAlkmT55Zk4knskMA41riTOZzNjRuJY41eVdDDHL7pgM8zGxmh7nOWZ57yWy91qu95u+aYkzmUw4fdESp5ot3ivIAMJ3JQi1GaZs+R4Z9gdF/N+XROKpZouPycrutStBqM0wZcv3yLDvVa/Yc9yiSvy/kaSvSPr/ktaXdLykWySdLyl6W65Us8V7BRnEZJhPNWDAI1t+jI3HLg4xNk2L/88GvgesDVwFnAe8FXgb8FXgkGjPidNkkEGM+D/VgAGvbPkeGfaHQfy/oZmdZmafA6aa2WwzW2BmpwEzOhm1i//PO3POmFbYgyaDDGLE/6kGDHhmy/fIsD8M4v/2AX3OiGOrdTJqF/8vePKeOClVn2g6yCBG/J9qwIBntvwYG49dHHqxiaFqwF4sabKZPWlmn2q9KOnVwG8bq1Wf8AgyiBH/pxow4JUt3yPD/rCI/z/T4fW7JH0v1mmq2eK9ggw8GKZs+R4Z9rP4vyTlJXGoOiZG6RSjWorBQ+nklS0/htAUtDEBBjFk8X8mM45pXPzvsecNxM1+oTYxIVkxM0xU8vUILW2MNjiUmLbEcIeDj5jZcqzJicQzmQEii/8zmQGib4nEU8z8n2q2+BZNn7NQHynbhJ7n2H7xCGRpJ3jASnqlmfUsuUkx83+q2eJbNH3OQn2kbBN6nmP7JfSaaTrz/3ojHusD10laV9J6UR5JN/N/qtniweeceQQleNqEnueYfvEIZGmnaoZ9GLh3xGubUCQUN+BVMU4HIfN/XbwE4x7nzCsowcsm9Dx7C/ljqBL//xOFBPFgM9vczDYH7i+fdxysg575PwQPwbjHOfMKSvAMfgg9z95C/hiq7hKfIum/gS9KWgAcB9UrhUHO/B9Lk4Jxj3PmFZTQj+CH0L7xEvLHUHnTyczuB94h6WDgSmBSLw5Tzvwfipdg3OOceQUleNmEnud+CPljqH2X2MwukXQlsAWApPea2VmN1awNL1F+qtniY/DalSBVQs9zbL94BLK0M64z/4filS0+Rs4Xc848pIleeAQZeEkTs/g/kxkSus6wkpbQRfxvZtOqHMSE13kJ2T1C0lKelYeJmBVGquy72d5xMyxZ/J/JJEVfxP+hekovXeyw6Y9T1Ph62YReMzHXmJdNO30R/4fqKb10scOmP05R4+tlE3rNxFxjXjar2NcqNcaE6im9dLHDpD9OWePrYRN6zcRcY1427VTdJd7PzH5QPp8CfAH4M2A+8I9mtqS2pzGiSV2sFx7645Q1vt6J1EP15zF6dS+bqhn239qenwIsAg4Crge+VtvLGNGkLtaTpvXHKWt8vROph+rPY/TqXjYQ9h12FzPboXz+RUnv6VRQ0lHAUQAnnXoiR7yv9+QUTeti+0FT+uOUNb6eWuJQ/XmMXt3LpkXVgH2lpI9S/O76ckmyP/5w23F2HuvM/x66WC889Mcpa3y9bEKvmRi9updNO1UD9r+A1h2VbwAbAA9J2giYF+ytJFRP6aWLHe/642Ei9JqJuca8bNrpRUtcS/yflU5Z6eTBeFE69fKzzgk92GYymQiy+D+TGSAaz/yfMqHL1ZjwqtA9XyAyVHD7cBOPfX+Gaaka0y8xX++6kcX/mcwAkcX/bcSI7D3a4hGU0CI1Ub6XTew15nUtt8ji/zZiRPYebfEISmiRmijfyyb2GvO6ll+0r1WqjTKZeE+kKv6HcJG9R1u8ghJSFOV72cSK8j36v52qzP+fk7RB+XwXSXcDcyXdK+nPa3sZQ0LF/6G/Mwtx+PYH8oFdD+W+ZYsaTSadWlL0mHMWe54hXZG9V780If4/wMxaV+zJwDvN7NXA3hTBAKPSnkj8vDPn1K5MFR7if69k0qklRU9dlO9l49UvTYn/V5e0upm9AKxlZtcDmNnvJE3sZDTWWmLwF/83mUw6xaToKYvyvWy8+qVJ8f+XgcslfQ74gaRTgQuBt9CDljgUL/G/RzLpVJOipyrK97Lx6pdGxf9mdpqkW4C/BbYqy28JXAR8Nry6BamK/2NE9h5t8QhK8CJVkX3sNeZ9LY9r8X9o8ukYpVNMWzySokNWOoXipXTK4v9MZkjI4v9MZpAws44PYAmwAzBjxGMm8EA32zoP4Kgmyw+bTar1yu33aYuZVQ7YM4A3djg2J8bhiPe4ocnyw2aTar1y+33aYmb9Ef9nMpk4+pJIPJPJxNHvAVs/7iuu/LDZpFovL5tU6xVjE+Mj/nfYTCbjT79n2EwmE0AesJnMANGXAStpP0m/lXSXpGNqlD9T0oOSaivxJU2XdJWk2yTdKukjNWxeJuk6STeVNrXUXJJWk/QbSZfVLH+PpFskzZN0Q02bqZIukHSHpNslvb6i/Nbl+7cej0v6hwqbfyzbPV/StyS9rEa9PlKWv7XT+4/Wf5LWk3SlpDvLv+vWsHlH6WelpF1q+jm5PGc3S/ofSVNr2Hy2LD9P0hWSpnUr33bsY5KsFUNe4eN4SQvb+ueto527lxDzW1AvD2A14PfAq4A1gZuA11TY7AHsBMwP8LMxsFP5fB3gdzX8iCLpHMAawFxgtxq+PgrMAS6rWbd7gA0Cz9s3gL8pn68JTA0854uBGV3KbAL8gSKMEuB84MiK9/1Tip0MJ1Go5n4EvLpO/wGfB44pnx8DzK5h8yfA1sDVFHs91fGzD7B6+Xx2TT8vb3v+YeCrVdciMB34IXDvyL7t4ON44OOh46cfM+yuwF1mdreZPQ98Gzikm4GZXQM8GuLEzBaZ2a/L508At1NclN1szMyeLP9do3x0vSsnaVPgAOD0kPqFUG71uQeFkAUze97MlgW8xV7A783s3opyqwNrSVqdYhA+UFH+T4C5Zva0FTHTPwVmjSzUof8OofgQovz7tiobM7vdzH7bqTIdbK4o6wbwK2DTGjaPt/27Nm3XQJdr8YvAJxjleom5fjvRjwG7CbCg7f/7qRhIvSJpJrAjxYxZVXY1SfOAB4ErzazK5j8oOmplQJUMuELSjeVOf1VsDjwEnFUuvU+XtHaAv8OAb3WtkNlC4N+B+yi2FX3MzK6oeN/5wJskrS9pEvBWipmmDhua2aLy+WJ8tOnvA2plMZB0oqQFwBHAZyrKHgIsNLPuKTZfyt+VS+8zR34l6MTQ33SSNBn4LvAPIz45R8XMVlixreamwK6S/rTLex8IPGhmNwZW641mthOwP/B/JVXF7a1OsaT6ipntCDxFsYysRNKawMHAdyrKrUsx620OTAPWlvTX3WzM7HaKZeYVwA8okhqsqFOvEe9jVKxkekXSscALwHk163SsmU0vy/9dl/edBHySikE9Cl8BtqDQ6i+iS8qldvoxYBey6qfwpuVrY46kNSgG63lmdmGIbbnkvAroFvX9BuBgSfdQLO3fIumbNd57Yfn3QeB/KL4mdON+4P622f4CigFch/2BX5vZkopyfwH8wcweMrPlFJlFdq96czM7w8x2NrM9KHaI+F3Nei2RtDFA+bex3bclHQkcCBxRfjiEcB7Qbde0LSg+5G4qr4NNgV+r2OGxI2a2pJwcVlLsEll1DQD9GbDXA1tK2rz89D8MuGSsnUgSxXe+283sCzVtXtG6iyhpLYpkc3d0Km9m/8/MNjWzmRTt+ImZdZ2VJK0taZ3Wc4qbIl3vfpvZYmCBpK3Ll/YCbqvTJuBdVCyHS+4DdpM0qTx3e1F87++KpFeWfzej+P5aN+veJUBrU/D3ABfXtAtC0n4UX1kONrOna9q0R6ofQvdr4BYze6WZzSyvg/spbnYurvDRnoj4L6m4Btoduj8ovuv8juJu8bE1yn+LYtmwvDwh769h80aKZdbNFEu1ecBbK2y2A35T2swHPhPQpj2pcZeY4u74TeXj1jrtL+12AG4o63YRsG4Nm7WBR4ApNX2cQHFxzgfOBSbWsLmW4sPjJmCvuv0HrA/8GLiT4u7yejVs/rJ8/hxF6OcPa9jcRXHPpHUNfLWGzXfLc3AzcCmwSd1rkVF+Aejg41zgltLHJcDGdfooSxMzmQFi6G86ZTLDRB6wmcwAkQdsJjNA5AGbyQwQecBmMgNEHrCZzACRB2wmM0D8L1yTiptNrWAlAAAAAElFTkSuQmCC\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for name, grid in grids.items():\n", + " if name == 'from_text': continue # skip the binary tile grid\n", + " imputed = rns.predict([grid])[0]\n", + "\n", + " plot_grid(grid, f\"Grid {name} original\")\n", + " plot_grid(imputed, f\"Grid {name} imputed (KNN)\")" + ], + "metadata": { + "collapsed": false, + "pycharm": { + "name": "#%%\n" + } + } + }, + { + "cell_type": "markdown", + "source": [ + "KNN can also be used to generate entirely novel grids, but this is subject to the same limitation faced by RNS: when a cell has no known neighbors, the cell value is randomly selected according to the known class distribution (preserving no spatial from the training set)." + ], + "metadata": { + "collapsed": false + } + }, + { + "cell_type": "code", + "execution_count": 13, + "outputs": [ + { + "data": { + "text/plain": "
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\n" 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\n" 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\n" 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\n" 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\n" 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\n" 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\n" + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "for name, grid in grids.items():\n", + " if name == 'from_text': continue # skip the binary tile grid\n", + " novel = np.zeros_like(grid).astype(int)\n", + " novel = knn.predict([novel])[0]\n", + "\n", + " plot_grid(grid, f\"Grid {name} original\")\n", + " plot_grid(novel, f\"Grid {name} generated same-size (KNN)\")\n", + "\n", + " # TODO demonstrate that class distribution is roughly equivalent while bond distribution is not?" + ], "metadata": { "collapsed": false, "pycharm": { @@ -1314,7 +1605,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "outputs": [], "source": [], "metadata": { @@ -1337,7 +1628,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 13, "outputs": [], "source": [], "metadata": { @@ -1363,7 +1654,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 14, "outputs": [ { "name": "stdout",