├── .ipynb_checkpoints
├── Untitled-checkpoint.ipynb
├── evolved-forest-demo-checkpoint.ipynb
├── evolved-forest-demo-implicit-fitness-checkpoint.ipynb
├── evolved-forest-demo-implicit-fitness-multi-oob-checkpoint.ipynb
├── evolved-forest-demo-original-checkpoint.ipynb
├── evolved-forest-demo-scaled-checkpoint.ipynb
├── evolved-forest-demo-variance-implicit-fitness-pressure-checkpoint.ipynb
├── evolved-forest-demo-variance-lexicase-checkpoint.ipynb
├── evolved-forest-demo-variance-only-checkpoint.ipynb
├── evolved-forest-demo-variance-only-objective-checkpoint.ipynb
├── evolved-forest-demo-variance-only-olson-Copy2-checkpoint.ipynb
├── evolved-forest-demo-variance-only-olson-checkpoint.ipynb
└── evolved-forest-demo-variance-only-olson-multi-oob-checkpoint.ipynb
├── README.md
├── datasets
├── GAMETES-easy-4x2-way_her-0.4_pop-1600_attribs-100_discrete.csv
└── spambase.csv
├── papers
└── Lexicase Selection.pdf
├── subsampling
├── .ipynb_checkpoints
│ ├── Difficulty Level of Rows - Approach - Results-checkpoint.ipynb
│ ├── evolved-forest-demo-checkpoint.ipynb
│ ├── evolved-forest-demo-variance-only-objective-checkpoint.ipynb
│ ├── evolved-forest-implicit-fitness-multi-oob-checkpoint.ipynb
│ ├── evolved-forest-multi-obj-variance-oob-score-checkpoint.ipynb
│ ├── evolved-forest-oob-variance-only-checkpoint.ipynb
│ ├── evolved-forest-single-obj-implicit-fitness-pressure-checkpoint.ipynb
│ ├── evolved-forest-single-obj-true-variance-checkpoint.ipynb
│ └── evolved-forest-variance-lexicase-checkpoint.ipynb
├── Difficulty Level of Rows - Approach - Results.ipynb
├── dump
│ ├── evolved-forest-demo-variance-only-objective.ipynb
│ ├── evolved-forest-demo-variance-only-olson-Copy2.ipynb
│ ├── evolved-forest-demo-variance-only.ipynb
│ └── evolved-forest-demo.ipynb
├── evolved-forest - Update levels after each row's evaluation-Multi-objectives.ipynb
├── evolved-forest-implicit-fitness-multi-oob.ipynb
├── evolved-forest-multi-obj-variance-oob-score.ipynb
├── evolved-forest-oob-variance-only.ipynb
├── evolved-forest-single-obj-implicit-fitness-pressure.ipynb
├── evolved-forest-single-obj-true-variance.ipynb
└── evolved-forest-variance-lexicase.ipynb
└── subspacing
├── .ipynb_checkpoints
└── evolved-forest-demo-variance-only-subspacing-checkpoint.ipynb
├── dataset_describe.py
├── dataset_describe.pyc
└── evolved-forest-demo-variance-only-subspacing.ipynb
/.ipynb_checkpoints/Untitled-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [],
3 | "metadata": {},
4 | "nbformat": 4,
5 | "nbformat_minor": 0
6 | }
7 |
--------------------------------------------------------------------------------
/.ipynb_checkpoints/evolved-forest-demo-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {
7 | "collapsed": false
8 | },
9 | "outputs": [],
10 | "source": [
11 | "import operator\n",
12 | "import itertools\n",
13 | "import numpy as np\n",
14 | "\n",
15 | "from deap import algorithms\n",
16 | "from deap import base\n",
17 | "from deap import creator\n",
18 | "from deap import tools\n",
19 | "from deap import gp\n",
20 | "\n",
21 | "from sklearn.tree import DecisionTreeClassifier\n",
22 | "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
23 | "from sklearn.datasets import load_digits\n",
24 | "from sklearn.cross_validation import train_test_split\n",
25 | "\n",
26 | "np.seterr(all='raise')\n",
27 | "\n",
28 | "digits = load_digits()\n",
29 | "digit_features, digit_labels = digits.data, digits.target\n",
30 | "X_train, X_test, y_train, y_test = train_test_split(digit_features, digit_labels, stratify=digit_labels,\n",
31 | " train_size=0.75, test_size=0.25)\n",
32 | "\n",
33 | "# defined a new primitive set for strongly typed GP\n",
34 | "pset = gp.PrimitiveSetTyped('MAIN', itertools.repeat(float, digit_features.shape[1]), bool, 'Feature')\n",
35 | "\n",
36 | "# boolean operators\n",
37 | "pset.addPrimitive(operator.and_, [bool, bool], bool)\n",
38 | "pset.addPrimitive(operator.or_, [bool, bool], bool)\n",
39 | "pset.addPrimitive(operator.not_, [bool], bool)\n",
40 | "\n",
41 | "# floating point operators\n",
42 | "# Define a protected division function\n",
43 | "def protectedDiv(left, right):\n",
44 | " try: return left / right\n",
45 | " except (ZeroDivisionError, FloatingPointError): return 1.\n",
46 | "\n",
47 | "pset.addPrimitive(operator.add, [float, float], float)\n",
48 | "pset.addPrimitive(operator.sub, [float, float], float)\n",
49 | "pset.addPrimitive(operator.mul, [float, float], float)\n",
50 | "pset.addPrimitive(protectedDiv, [float, float], float)\n",
51 | "\n",
52 | "# logic operators\n",
53 | "# Define a new if-then-else function\n",
54 | "def if_then_else(in1, output1, output2):\n",
55 | " if in1: return output1\n",
56 | " else: return output2\n",
57 | "\n",
58 | "pset.addPrimitive(operator.lt, [float, float], bool)\n",
59 | "pset.addPrimitive(operator.eq, [float, float], bool)\n",
60 | "pset.addPrimitive(if_then_else, [bool, float, float], float)\n",
61 | "\n",
62 | "# terminals\n",
63 | "pset.addTerminal(False, bool)\n",
64 | "pset.addTerminal(True, bool)\n",
65 | "for val in np.arange(-10., 11.):\n",
66 | " pset.addTerminal(val, float)\n",
67 | "\n",
68 | "creator.create('FitnessMax', base.Fitness, weights=(1.0,))\n",
69 | "creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMax)\n",
70 | "\n",
71 | "toolbox = base.Toolbox()\n",
72 | "toolbox.register('expr', gp.genHalfAndHalf, pset=pset, min_=1, max_=3)\n",
73 | "toolbox.register('individual', tools.initIterate, creator.Individual, toolbox.expr)\n",
74 | "toolbox.register('population', tools.initRepeat, list, toolbox.individual)\n",
75 | "toolbox.register('compile', gp.compile, pset=pset)\n",
76 | "\n",
77 | "def evaluate_individual(individual):\n",
78 | " # Transform the tree expression into a callable function\n",
79 | " func = toolbox.compile(expr=individual)\n",
80 | " subsample = np.array([func(*record) for record in X_train])\n",
81 | " \n",
82 | " if X_train[subsample].shape[0] == 0:\n",
83 | " return 1e-20,\n",
84 | " \n",
85 | " clf = DecisionTreeClassifier(random_state=34092)\n",
86 | " clf.fit(X_train[subsample], y_train[subsample])\n",
87 | " score = clf.score(X_test, y_test)\n",
88 | " \n",
89 | " return score,\n",
90 | " \n",
91 | "toolbox.register('evaluate', evaluate_individual)\n",
92 | "toolbox.register('select', tools.selTournament, tournsize=3)\n",
93 | "toolbox.register('mate', gp.cxOnePoint)\n",
94 | "toolbox.register('expr_mut', gp.genFull, min_=0, max_=3)\n",
95 | "toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut, pset=pset)\n",
96 | "\n",
97 | "population = toolbox.population(n=100)\n",
98 | "halloffame = tools.HallOfFame(1)\n",
99 | "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
100 | "stats.register('std', np.std)\n",
101 | "stats.register('min', np.min)\n",
102 | "stats.register('avg', np.mean)\n",
103 | "stats.register('max', np.max)\n",
104 | "\n",
105 | "clf = DecisionTreeClassifier(random_state=34092)\n",
106 | "clf.fit(X_train, y_train)\n",
107 | "print('Base DecisionTreeClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
108 | "\n",
109 | "clf = RandomForestClassifier(random_state=34092)\n",
110 | "clf.fit(X_train, y_train)\n",
111 | "print('Base RandomForestClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
112 | "\n",
113 | "clf = GradientBoostingClassifier(random_state=34092)\n",
114 | "clf.fit(X_train, y_train)\n",
115 | "print('Base GradientBoostingClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
116 | "\n",
117 | "print('')\n",
118 | "\n",
119 | "cxpb = 0.5\n",
120 | "mutpb = 0.5\n",
121 | "ngen = 50\n",
122 | "verbose = True\n",
123 | "\n",
124 | "logbook = tools.Logbook()\n",
125 | "logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])\n",
126 | "\n",
127 | "# Evaluate the individuals with an invalid fitness\n",
128 | "invalid_ind = [ind for ind in population if not ind.fitness.valid]\n",
129 | "fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n",
130 | "for ind, fit in zip(invalid_ind, fitnesses):\n",
131 | " ind.fitness.values = fit\n",
132 | "\n",
133 | "if halloffame is not None:\n",
134 | " halloffame.update(population)\n",
135 | "\n",
136 | "record = stats.compile(population) if stats else {}\n",
137 | "logbook.record(gen=0, nevals=len(invalid_ind), **record)\n",
138 | "if verbose:\n",
139 | " print(logbook.stream)\n",
140 | "\n",
141 | "# Begin the generational process\n",
142 | "for gen in range(1, ngen + 1):\n",
143 | " # Select the next generation individuals\n",
144 | " offspring = toolbox.select(population, len(population))\n",
145 | "\n",
146 | " # Vary the pool of individuals\n",
147 | " offspring = algorithms.varAnd(offspring, toolbox, cxpb, mutpb)\n",
148 | "\n",
149 | " # Evaluate the individuals with an invalid fitness\n",
150 | " invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n",
151 | " fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n",
152 | " for ind, fit in zip(invalid_ind, fitnesses):\n",
153 | " ind.fitness.values = fit\n",
154 | "\n",
155 | " # Update the hall of fame with the generated individuals\n",
156 | " if halloffame is not None:\n",
157 | " halloffame.update(offspring)\n",
158 | "\n",
159 | " # Replace the current population by the offspring\n",
160 | " population[:] = offspring\n",
161 | "\n",
162 | " # Append the current generation statistics to the logbook\n",
163 | " record = stats.compile(population) if stats else {}\n",
164 | " logbook.record(gen=gen, nevals=len(invalid_ind), **record)\n",
165 | " if verbose:\n",
166 | " print(logbook.stream)\n",
167 | "\n",
168 | "str(halloffame[0])"
169 | ]
170 | },
171 | {
172 | "cell_type": "code",
173 | "execution_count": null,
174 | "metadata": {
175 | "collapsed": false
176 | },
177 | "outputs": [],
178 | "source": [
179 | "forest_predictions = []\n",
180 | "\n",
181 | "for ind_num, individual in enumerate(pop):\n",
182 | " func = toolbox.compile(expr=individual)\n",
183 | " subsample = np.array([func(*record) for record in X_train])\n",
184 | " \n",
185 | " if X_train[subsample].shape[0] == 0:\n",
186 | " continue\n",
187 | " \n",
188 | " clf = DecisionTreeClassifier(random_state=34092)\n",
189 | " clf.fit(X_train[subsample], y_train[subsample])\n",
190 | " predictions = clf.predict(X_test)\n",
191 | " forest_predictions.append(predictions)"
192 | ]
193 | },
194 | {
195 | "cell_type": "code",
196 | "execution_count": null,
197 | "metadata": {
198 | "collapsed": false,
199 | "scrolled": false
200 | },
201 | "outputs": [],
202 | "source": [
203 | "from collections import Counter\n",
204 | "from sklearn.metrics import accuracy_score\n",
205 | "\n",
206 | "y_pred = np.array(\n",
207 | " [Counter(instance_forest_predictions).most_common(1)[0][0] for instance_forest_predictions in zip(*forest_predictions)])\n",
208 | "np.sum(y_test == y_pred) / len(y_test)"
209 | ]
210 | },
211 | {
212 | "cell_type": "code",
213 | "execution_count": null,
214 | "metadata": {
215 | "collapsed": true
216 | },
217 | "outputs": [],
218 | "source": []
219 | }
220 | ],
221 | "metadata": {
222 | "kernelspec": {
223 | "display_name": "Python 3",
224 | "language": "python",
225 | "name": "python3"
226 | },
227 | "language_info": {
228 | "codemirror_mode": {
229 | "name": "ipython",
230 | "version": 3
231 | },
232 | "file_extension": ".py",
233 | "mimetype": "text/x-python",
234 | "name": "python",
235 | "nbconvert_exporter": "python",
236 | "pygments_lexer": "ipython3",
237 | "version": "3.5.1"
238 | }
239 | },
240 | "nbformat": 4,
241 | "nbformat_minor": 0
242 | }
243 |
--------------------------------------------------------------------------------
/.ipynb_checkpoints/evolved-forest-demo-original-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {
7 | "collapsed": false
8 | },
9 | "outputs": [
10 | {
11 | "name": "stdout",
12 | "output_type": "stream",
13 | "text": [
14 | "Base DecisionTreeClassifier accuracy: 0.869179600887\n",
15 | "Base RandomForestClassifier accuracy: 0.946784922395\n",
16 | "Base GradientBoostingClassifier accuracy: 0.953436807095\n",
17 | "\n",
18 | "gen\tnevals\tstd \tmin \tavg \tmax \n",
19 | "0 \t100 \t0.379036\t1e-20\t0.394612\t0.875831\n",
20 | "1 \t85 \t0.337387\t1e-20\t0.584945\t0.878049\n",
21 | "2 \t76 \t0.300443\t1e-20\t0.709135\t0.873614\n",
22 | "3 \t73 \t0.311377\t1e-20\t0.707761\t0.873614\n",
23 | "4 \t75 \t0.251842\t1e-20\t0.774656\t0.875831\n",
24 | "5 \t73 \t0.268957\t1e-20\t0.754745\t0.880266\n",
25 | "6 \t71 \t0.21039 \t1e-20\t0.80051 \t0.882483\n",
26 | "7 \t78 \t0.209782\t1e-20\t0.811175\t0.882483\n",
27 | "8 \t77 \t0.125856\t1e-20\t0.852106\t0.891353\n",
28 | "9 \t71 \t0.132058\t1e-20\t0.843259\t0.891353\n",
29 | "10 \t76 \t0.0994663\t1e-20\t0.855277\t0.891353"
30 | ]
31 | }
32 | ],
33 | "source": [
34 | "import operator\n",
35 | "import itertools\n",
36 | "import numpy as np\n",
37 | "\n",
38 | "from deap import algorithms\n",
39 | "from deap import base\n",
40 | "from deap import creator\n",
41 | "from deap import tools\n",
42 | "from deap import gp\n",
43 | "\n",
44 | "from sklearn.tree import DecisionTreeClassifier\n",
45 | "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
46 | "from sklearn.datasets import load_digits\n",
47 | "from sklearn.cross_validation import train_test_split\n",
48 | "\n",
49 | "np.seterr(all='raise')\n",
50 | "\n",
51 | "digits = load_digits()\n",
52 | "digit_features, digit_labels = digits.data, digits.target\n",
53 | "X_train, X_test, y_train, y_test = train_test_split(digit_features, digit_labels, stratify=digit_labels,\n",
54 | " train_size=0.75, test_size=0.25)\n",
55 | "\n",
56 | "# defined a new primitive set for strongly typed GP\n",
57 | "pset = gp.PrimitiveSetTyped('MAIN', itertools.repeat(float, digit_features.shape[1]), bool, 'Feature')\n",
58 | "\n",
59 | "# boolean operators\n",
60 | "pset.addPrimitive(operator.and_, [bool, bool], bool)\n",
61 | "pset.addPrimitive(operator.or_, [bool, bool], bool)\n",
62 | "pset.addPrimitive(operator.not_, [bool], bool)\n",
63 | "\n",
64 | "# floating point operators\n",
65 | "# Define a protected division function\n",
66 | "def protectedDiv(left, right):\n",
67 | " try: return left / right\n",
68 | " except (ZeroDivisionError, FloatingPointError): return 1.\n",
69 | "\n",
70 | "pset.addPrimitive(operator.add, [float, float], float)\n",
71 | "pset.addPrimitive(operator.sub, [float, float], float)\n",
72 | "pset.addPrimitive(operator.mul, [float, float], float)\n",
73 | "pset.addPrimitive(protectedDiv, [float, float], float)\n",
74 | "\n",
75 | "# logic operators\n",
76 | "# Define a new if-then-else function\n",
77 | "def if_then_else(in1, output1, output2):\n",
78 | " if in1: return output1\n",
79 | " else: return output2\n",
80 | "\n",
81 | "pset.addPrimitive(operator.lt, [float, float], bool)\n",
82 | "pset.addPrimitive(operator.eq, [float, float], bool)\n",
83 | "pset.addPrimitive(if_then_else, [bool, float, float], float)\n",
84 | "\n",
85 | "# terminals\n",
86 | "pset.addTerminal(False, bool)\n",
87 | "pset.addTerminal(True, bool)\n",
88 | "for val in np.arange(-10., 11.):\n",
89 | " pset.addTerminal(val, float)\n",
90 | "\n",
91 | "creator.create('FitnessMax', base.Fitness, weights=(1.0,))\n",
92 | "creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMax)\n",
93 | "\n",
94 | "toolbox = base.Toolbox()\n",
95 | "toolbox.register('expr', gp.genHalfAndHalf, pset=pset, min_=1, max_=3)\n",
96 | "toolbox.register('individual', tools.initIterate, creator.Individual, toolbox.expr)\n",
97 | "toolbox.register('population', tools.initRepeat, list, toolbox.individual)\n",
98 | "toolbox.register('compile', gp.compile, pset=pset)\n",
99 | "\n",
100 | "def evaluate_individual(individual):\n",
101 | " # Transform the tree expression into a callable function\n",
102 | " func = toolbox.compile(expr=individual)\n",
103 | " subsample = np.array([func(*record) for record in X_train])\n",
104 | " \n",
105 | " if X_train[subsample].shape[0] == 0:\n",
106 | " return 1e-20,\n",
107 | " \n",
108 | " clf = DecisionTreeClassifier(random_state=34092)\n",
109 | " clf.fit(X_train[subsample], y_train[subsample])\n",
110 | " score = clf.score(X_test, y_test)\n",
111 | " \n",
112 | " return score,\n",
113 | " \n",
114 | "toolbox.register('evaluate', evaluate_individual)\n",
115 | "toolbox.register('select', tools.selTournament, tournsize=3)\n",
116 | "toolbox.register('mate', gp.cxOnePoint)\n",
117 | "toolbox.register('expr_mut', gp.genFull, min_=0, max_=3)\n",
118 | "toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut, pset=pset)\n",
119 | "\n",
120 | "population = toolbox.population(n=100)\n",
121 | "halloffame = tools.HallOfFame(1)\n",
122 | "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
123 | "stats.register('std', np.std)\n",
124 | "stats.register('min', np.min)\n",
125 | "stats.register('avg', np.mean)\n",
126 | "stats.register('max', np.max)\n",
127 | "\n",
128 | "clf = DecisionTreeClassifier(random_state=34092)\n",
129 | "clf.fit(X_train, y_train)\n",
130 | "print('Base DecisionTreeClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
131 | "\n",
132 | "clf = RandomForestClassifier(random_state=34092)\n",
133 | "clf.fit(X_train, y_train)\n",
134 | "print('Base RandomForestClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
135 | "\n",
136 | "clf = GradientBoostingClassifier(random_state=34092)\n",
137 | "clf.fit(X_train, y_train)\n",
138 | "print('Base GradientBoostingClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
139 | "\n",
140 | "print('')\n",
141 | "\n",
142 | "cxpb = 0.5\n",
143 | "mutpb = 0.5\n",
144 | "ngen = 50\n",
145 | "verbose = True\n",
146 | "\n",
147 | "logbook = tools.Logbook()\n",
148 | "logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])\n",
149 | "\n",
150 | "# Evaluate the individuals with an invalid fitness\n",
151 | "invalid_ind = [ind for ind in population if not ind.fitness.valid]\n",
152 | "fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n",
153 | "for ind, fit in zip(invalid_ind, fitnesses):\n",
154 | " ind.fitness.values = fit\n",
155 | "\n",
156 | "if halloffame is not None:\n",
157 | " halloffame.update(population)\n",
158 | "\n",
159 | "record = stats.compile(population) if stats else {}\n",
160 | "logbook.record(gen=0, nevals=len(invalid_ind), **record)\n",
161 | "if verbose:\n",
162 | " print(logbook.stream)\n",
163 | "\n",
164 | "# Begin the generational process\n",
165 | "for gen in range(1, ngen + 1):\n",
166 | " # Select the next generation individuals\n",
167 | " offspring = toolbox.select(population, len(population))\n",
168 | "\n",
169 | " # Vary the pool of individuals\n",
170 | " offspring = algorithms.varAnd(offspring, toolbox, cxpb, mutpb)\n",
171 | "\n",
172 | " # Evaluate the individuals with an invalid fitness\n",
173 | " invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n",
174 | " fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n",
175 | " for ind, fit in zip(invalid_ind, fitnesses):\n",
176 | " ind.fitness.values = fit\n",
177 | "\n",
178 | " # Update the hall of fame with the generated individuals\n",
179 | " if halloffame is not None:\n",
180 | " halloffame.update(offspring)\n",
181 | "\n",
182 | " # Replace the current population by the offspring\n",
183 | " population[:] = offspring\n",
184 | "\n",
185 | " # Append the current generation statistics to the logbook\n",
186 | " record = stats.compile(population) if stats else {}\n",
187 | " logbook.record(gen=gen, nevals=len(invalid_ind), **record)\n",
188 | " if verbose:\n",
189 | " print(logbook.stream)\n",
190 | "\n",
191 | "str(halloffame[0])"
192 | ]
193 | },
194 | {
195 | "cell_type": "code",
196 | "execution_count": null,
197 | "metadata": {
198 | "collapsed": false
199 | },
200 | "outputs": [],
201 | "source": [
202 | "forest_predictions = []\n",
203 | "\n",
204 | "for ind_num, individual in enumerate(pop):\n",
205 | " func = toolbox.compile(expr=individual)\n",
206 | " subsample = np.array([func(*record) for record in X_train])\n",
207 | " \n",
208 | " if X_train[subsample].shape[0] == 0:\n",
209 | " continue\n",
210 | " \n",
211 | " clf = DecisionTreeClassifier(random_state=34092)\n",
212 | " clf.fit(X_train[subsample], y_train[subsample])\n",
213 | " predictions = clf.predict(X_test)\n",
214 | " forest_predictions.append(predictions)"
215 | ]
216 | },
217 | {
218 | "cell_type": "code",
219 | "execution_count": null,
220 | "metadata": {
221 | "collapsed": false,
222 | "scrolled": false
223 | },
224 | "outputs": [],
225 | "source": [
226 | "from collections import Counter\n",
227 | "from sklearn.metrics import accuracy_score\n",
228 | "\n",
229 | "y_pred = np.array(\n",
230 | " [Counter(instance_forest_predictions).most_common(1)[0][0] for instance_forest_predictions in zip(*forest_predictions)])\n",
231 | "np.sum(y_test == y_pred) / len(y_test)"
232 | ]
233 | },
234 | {
235 | "cell_type": "code",
236 | "execution_count": null,
237 | "metadata": {
238 | "collapsed": true
239 | },
240 | "outputs": [],
241 | "source": []
242 | }
243 | ],
244 | "metadata": {
245 | "kernelspec": {
246 | "display_name": "Python 2",
247 | "language": "python",
248 | "name": "python2"
249 | },
250 | "language_info": {
251 | "codemirror_mode": {
252 | "name": "ipython",
253 | "version": 2
254 | },
255 | "file_extension": ".py",
256 | "mimetype": "text/x-python",
257 | "name": "python",
258 | "nbconvert_exporter": "python",
259 | "pygments_lexer": "ipython2",
260 | "version": "2.7.9"
261 | }
262 | },
263 | "nbformat": 4,
264 | "nbformat_minor": 0
265 | }
266 |
--------------------------------------------------------------------------------
/.ipynb_checkpoints/evolved-forest-demo-scaled-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": null,
6 | "metadata": {
7 | "collapsed": false
8 | },
9 | "outputs": [],
10 | "source": [
11 | "import operator\n",
12 | "import itertools\n",
13 | "import numpy as np\n",
14 | "\n",
15 | "from deap import algorithms\n",
16 | "from deap import base\n",
17 | "from deap import creator\n",
18 | "from deap import tools\n",
19 | "from deap import gp\n",
20 | "\n",
21 | "from sklearn.tree import DecisionTreeClassifier\n",
22 | "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
23 | "from sklearn.datasets import load_digits\n",
24 | "from sklearn.cross_validation import train_test_split\n",
25 | "\n",
26 | "np.seterr(all='raise')\n",
27 | "\n",
28 | "digits = load_digits()\n",
29 | "digit_features, digit_labels = digits.data, digits.target\n",
30 | "X_train, X_test, y_train, y_test = train_test_split(digit_features, digit_labels, stratify=digit_labels,\n",
31 | " train_size=0.75, test_size=0.25)\n",
32 | "\n",
33 | "# defined a new primitive set for strongly typed GP\n",
34 | "pset = gp.PrimitiveSetTyped('MAIN', itertools.repeat(float, digit_features.shape[1]), bool, 'Feature')\n",
35 | "\n",
36 | "# boolean operators\n",
37 | "pset.addPrimitive(operator.and_, [bool, bool], bool)\n",
38 | "pset.addPrimitive(operator.or_, [bool, bool], bool)\n",
39 | "pset.addPrimitive(operator.not_, [bool], bool)\n",
40 | "\n",
41 | "# floating point operators\n",
42 | "# Define a protected division function\n",
43 | "def protectedDiv(left, right):\n",
44 | " try: return left / right\n",
45 | " except (ZeroDivisionError, FloatingPointError): return 1.\n",
46 | "\n",
47 | "pset.addPrimitive(operator.add, [float, float], float)\n",
48 | "pset.addPrimitive(operator.sub, [float, float], float)\n",
49 | "pset.addPrimitive(operator.mul, [float, float], float)\n",
50 | "pset.addPrimitive(protectedDiv, [float, float], float)\n",
51 | "\n",
52 | "# logic operators\n",
53 | "# Define a new if-then-else function\n",
54 | "def if_then_else(in1, output1, output2):\n",
55 | " if in1: return output1\n",
56 | " else: return output2\n",
57 | "\n",
58 | "pset.addPrimitive(operator.lt, [float, float], bool)\n",
59 | "pset.addPrimitive(operator.eq, [float, float], bool)\n",
60 | "pset.addPrimitive(if_then_else, [bool, float, float], float)\n",
61 | "\n",
62 | "# terminals\n",
63 | "pset.addTerminal(False, bool)\n",
64 | "pset.addTerminal(True, bool)\n",
65 | "for val in np.arange(-10., 11.):\n",
66 | " pset.addTerminal(val, float)\n",
67 | "\n",
68 | "creator.create('FitnessMax', base.Fitness, weights=(1.0,))\n",
69 | "creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMax)\n",
70 | "\n",
71 | "toolbox = base.Toolbox()\n",
72 | "toolbox.register('expr', gp.genHalfAndHalf, pset=pset, min_=1, max_=3)\n",
73 | "toolbox.register('individual', tools.initIterate, creator.Individual, toolbox.expr)\n",
74 | "toolbox.register('population', tools.initRepeat, list, toolbox.individual)\n",
75 | "toolbox.register('compile', gp.compile, pset=pset)\n",
76 | "\n",
77 | "def evaluate_individual(individual):\n",
78 | " # Transform the tree expression into a callable function\n",
79 | " func = toolbox.compile(expr=individual)\n",
80 | " subsample = np.array([func(*record) for record in X_train])\n",
81 | " \n",
82 | " if X_train[subsample].shape[0] == 0:\n",
83 | " return 1e-20,\n",
84 | " \n",
85 | " clf = DecisionTreeClassifier(random_state=34092)\n",
86 | " clf.fit(X_train[subsample], y_train[subsample])\n",
87 | " score = clf.score(X_test, y_test)\n",
88 | " \n",
89 | " return score,\n",
90 | " \n",
91 | "toolbox.register('evaluate', evaluate_individual)\n",
92 | "toolbox.register('select', tools.selTournament, tournsize=3)\n",
93 | "toolbox.register('mate', gp.cxOnePoint)\n",
94 | "toolbox.register('expr_mut', gp.genFull, min_=0, max_=3)\n",
95 | "toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut, pset=pset)\n",
96 | "\n",
97 | "population = toolbox.population(n=100)\n",
98 | "halloffame = tools.HallOfFame(1)\n",
99 | "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
100 | "stats.register('std', np.std)\n",
101 | "stats.register('min', np.min)\n",
102 | "stats.register('avg', np.mean)\n",
103 | "stats.register('max', np.max)\n",
104 | "\n",
105 | "clf = DecisionTreeClassifier(random_state=34092)\n",
106 | "clf.fit(X_train, y_train)\n",
107 | "print('Base DecisionTreeClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
108 | "\n",
109 | "clf = RandomForestClassifier(random_state=34092)\n",
110 | "clf.fit(X_train, y_train)\n",
111 | "print('Base RandomForestClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
112 | "\n",
113 | "clf = GradientBoostingClassifier(random_state=34092)\n",
114 | "clf.fit(X_train, y_train)\n",
115 | "print('Base GradientBoostingClassifier accuracy: {}'.format(clf.score(X_test, y_test)))\n",
116 | "\n",
117 | "print('')\n",
118 | "\n",
119 | "cxpb = 0.5\n",
120 | "mutpb = 0.5\n",
121 | "ngen = 50\n",
122 | "verbose = True\n",
123 | "\n",
124 | "logbook = tools.Logbook()\n",
125 | "logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])\n",
126 | "\n",
127 | "# Evaluate the individuals with an invalid fitness\n",
128 | "invalid_ind = [ind for ind in population if not ind.fitness.valid]\n",
129 | "fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n",
130 | "for ind, fit in zip(invalid_ind, fitnesses):\n",
131 | " ind.fitness.values = fit\n",
132 | "\n",
133 | "if halloffame is not None:\n",
134 | " halloffame.update(population)\n",
135 | "\n",
136 | "record = stats.compile(population) if stats else {}\n",
137 | "logbook.record(gen=0, nevals=len(invalid_ind), **record)\n",
138 | "if verbose:\n",
139 | " print(logbook.stream)\n",
140 | "\n",
141 | "# Begin the generational process\n",
142 | "for gen in range(1, ngen + 1):\n",
143 | " # Select the next generation individuals\n",
144 | " offspring = toolbox.select(population, len(population))\n",
145 | "\n",
146 | " # Vary the pool of individuals\n",
147 | " offspring = algorithms.varAnd(offspring, toolbox, cxpb, mutpb)\n",
148 | "\n",
149 | " # Evaluate the individuals with an invalid fitness\n",
150 | " invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n",
151 | " fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n",
152 | " for ind, fit in zip(invalid_ind, fitnesses):\n",
153 | " ind.fitness.values = fit\n",
154 | "\n",
155 | " # Update the hall of fame with the generated individuals\n",
156 | " if halloffame is not None:\n",
157 | " halloffame.update(offspring)\n",
158 | "\n",
159 | " # Replace the current population by the offspring\n",
160 | " population[:] = offspring\n",
161 | "\n",
162 | " # Append the current generation statistics to the logbook\n",
163 | " record = stats.compile(population) if stats else {}\n",
164 | " logbook.record(gen=gen, nevals=len(invalid_ind), **record)\n",
165 | " if verbose:\n",
166 | " print(logbook.stream)\n",
167 | "\n",
168 | "str(halloffame[0])"
169 | ]
170 | },
171 | {
172 | "cell_type": "code",
173 | "execution_count": null,
174 | "metadata": {
175 | "collapsed": false
176 | },
177 | "outputs": [],
178 | "source": [
179 | "forest_predictions = []\n",
180 | "\n",
181 | "for ind_num, individual in enumerate(pop):\n",
182 | " func = toolbox.compile(expr=individual)\n",
183 | " subsample = np.array([func(*record) for record in X_train])\n",
184 | " \n",
185 | " if X_train[subsample].shape[0] == 0:\n",
186 | " continue\n",
187 | " \n",
188 | " clf = DecisionTreeClassifier(random_state=34092)\n",
189 | " clf.fit(X_train[subsample], y_train[subsample])\n",
190 | " predictions = clf.predict(X_test)\n",
191 | " forest_predictions.append(predictions)"
192 | ]
193 | },
194 | {
195 | "cell_type": "code",
196 | "execution_count": null,
197 | "metadata": {
198 | "collapsed": false,
199 | "scrolled": false
200 | },
201 | "outputs": [],
202 | "source": [
203 | "from collections import Counter\n",
204 | "from sklearn.metrics import accuracy_score\n",
205 | "\n",
206 | "y_pred = np.array(\n",
207 | " [Counter(instance_forest_predictions).most_common(1)[0][0] for instance_forest_predictions in zip(*forest_predictions)])\n",
208 | "np.sum(y_test == y_pred) / len(y_test)"
209 | ]
210 | },
211 | {
212 | "cell_type": "code",
213 | "execution_count": null,
214 | "metadata": {
215 | "collapsed": true
216 | },
217 | "outputs": [],
218 | "source": []
219 | }
220 | ],
221 | "metadata": {
222 | "kernelspec": {
223 | "display_name": "Python 3",
224 | "language": "python",
225 | "name": "python3"
226 | },
227 | "language_info": {
228 | "codemirror_mode": {
229 | "name": "ipython",
230 | "version": 3
231 | },
232 | "file_extension": ".py",
233 | "mimetype": "text/x-python",
234 | "name": "python",
235 | "nbconvert_exporter": "python",
236 | "pygments_lexer": "ipython3",
237 | "version": "3.5.1"
238 | }
239 | },
240 | "nbformat": 4,
241 | "nbformat_minor": 0
242 | }
243 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | #Evolved Forest
2 |
3 | Using Genetic Programming to grow forests for regression and classification. Metafeatures are used to reduce the exploration space and aid the GP to come with the better informed forests.
4 | Hunch is to see if GP can grow better trees than randomised generation of forests.
5 |
6 | Repo contains notebooks for different experiments.
7 |
8 | ## Subsampling
9 | Currently subspacing based methods have been explored. Each tree in the GP decides which rows to keep based on simple rules.
10 | Eg.
11 | ```
12 | Feature_3 should be greater than 3 and Feature_4 < 2.
13 | ```
14 | ## Subspacing
15 | [todo] Feature level metafeatures need to be extracted and made as primitives to be fed to GP.
16 |
17 | ##Fitness and Selection methods being explored
18 | - true_probablity variance
19 | - Custom difficulty scores per row.
20 | - Lexicase Selection
21 | - Implicit Fitness pressure.
22 | - Private Holdout Fitness
23 | - Out of Bag Error Fitness
24 |
25 |
26 |
27 | Multi-objective evolution and Pareto front are also being explored combining above methods.
28 |
29 |
30 |
--------------------------------------------------------------------------------
/datasets/GAMETES-easy-4x2-way_her-0.4_pop-1600_attribs-100_discrete.csv:
--------------------------------------------------------------------------------
1 | N0 N1 N2 N3 N4 N5 N6 N7 N8 N9 N10 N11 N12 N13 N14 N15 N16 N17 N18 N19 N20 N21 N22 N23 N24 N25 N26 N27 N28 N29 N30 N31 N32 N33 N34 N35 N36 N37 N38 N39 N40 N41 N42 N43 N44 N45 N46 N47 N48 N49 N50 N51 N52 N53 N54 N55 N56 N57 N58 N59 N60 N61 N62 N63 N64 N65 N66 N67 N68 N69 N70 N71 N72 N73 N74 N75 N76 N77 N78 N79 N80 N81 N82 N83 N84 N85 N86 N87 N88 N89 N90 N91 M0P0 M0P1 M1P0 M1P1 M2P0 M2P1 M3P0 M3P1 class
2 | 1 1 1 0 1 1 1 1 0 0 1 0 0 1 1 1 0 0 2 0 0 0 1 0 0 1 0 0 1 1 1 1 1 0 1 2 1 0 1 1 1 1 1 0 1 0 0 1 2 0 1 0 0 1 0 1 2 1 0 0 0 1 0 0 0 0 1 0 0 1 1 0 1 0 0 1 0 2 1 1 0 1 2 0 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 1 1
3 | 0 0 0 1 0 1 1 1 0 0 0 1 0 0 2 1 2 0 0 1 2 0 0 0 1 1 0 0 1 1 1 1 0 0 1 1 0 1 1 1 1 0 0 0 1 2 1 0 1 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 0 1 1 0 2 1 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 1 0 1 1 1 1 0 0 0 0 1
4 | 1 0 1 1 0 2 1 1 0 0 0 0 0 0 0 0 1 2 0 2 0 2 0 1 0 0 1 0 0 0 1 0 1 1 1 0 0 0 0 2 1 1 1 1 0 1 0 0 1 2 0 0 0 0 1 2 0 0 1 0 0 1 0 1 0 1 1 0 0 2 0 0 0 1 0 1 1 1 1 1 2 0 0 0 0 1 1 2 2 1 1 0 0 0 0 0 0 2 1 1 1
5 | 0 2 0 0 1 1 0 0 1 1 1 0 0 1 0 2 0 1 0 1 1 1 1 0 1 0 1 1 0 0 1 2 0 1 0 0 0 1 1 0 1 0 2 0 0 1 1 1 0 1 1 0 0 1 0 0 2 0 0 0 0 0 0 0 1 0 2 1 1 1 0 0 0 1 0 1 0 0 2 0 1 0 0 1 0 0 1 1 1 0 0 1 1 2 0 0 1 1 0 1 1
6 | 1 0 1 2 0 1 1 2 0 1 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 2 0 1 1 1 0 2 1 1 1 1 2 1 0 0 1 0 0 0 0 1 0 2 1 0 0 0 0 1 1 1 1 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 0 1 0 2 0 1 1 0 0 1 0 0 0 1 0 1 1 1 0 0 1 0 0 1 1 1 0 1 1
7 | 1 1 1 1 0 1 1 1 0 0 0 1 0 1 0 0 1 1 1 0 0 0 0 1 1 2 0 0 0 0 2 0 1 1 0 0 0 1 0 0 1 1 0 1 0 0 1 0 0 0 0 1 0 1 0 1 1 0 1 0 1 2 0 1 2 0 1 0 2 1 0 1 0 1 2 0 0 1 1 0 0 1 0 0 2 2 0 0 0 0 0 0 1 0 1 1 0 1 1 1 1
8 | 0 0 0 0 1 1 0 0 1 1 1 1 0 2 1 1 0 1 0 0 2 0 0 0 0 0 0 1 1 0 1 0 0 2 2 0 0 0 1 1 1 2 0 0 0 0 2 1 0 0 0 1 0 0 1 1 2 0 0 1 1 1 0 1 1 0 0 1 1 1 1 1 0 0 0 0 1 1 2 0 0 0 1 0 1 1 0 2 1 0 2 1 0 1 0 0 0 1 2 1 1
9 | 1 0 1 0 0 0 0 1 1 1 0 0 1 1 1 0 2 0 0 1 1 1 1 1 1 1 1 0 0 0 0 1 0 1 1 0 2 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 2 1 1 1 0 1 0 1 1 1 1 1 0 0 1 0 0 2 2 0 0 0 1 0 0 1 0 1 0 1 0 1 1 1 0 1 1 1 1 0 0 0 0 0 0 1 0 1 1
10 | 0 2 0 0 1 2 1 0 0 2 0 1 0 1 2 1 0 1 1 1 1 2 0 1 1 0 0 0 1 0 1 1 0 0 0 0 0 0 0 2 2 0 0 1 1 0 1 0 0 1 1 1 1 0 1 1 2 1 1 1 1 0 0 2 1 1 0 0 0 0 0 1 1 0 0 1 0 1 1 0 0 1 1 0 0 0 1 1 2 1 1 0 1 2 1 1 0 0 1 1 1
11 | 0 0 0 0 0 0 1 0 1 1 1 0 1 1 0 1 1 2 1 0 0 0 1 1 0 0 1 1 0 1 0 2 0 0 1 1 0 0 1 1 1 1 1 2 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 2 0 2 0 1 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 2 0 0 0 0 0 1 1 2 1 0 0 0 0 0 1 1 1 2 1
12 | 1 2 0 0 1 1 0 1 1 1 0 1 0 0 0 0 0 1 0 1 1 0 0 0 1 1 2 0 0 0 0 0 0 0 0 0 0 2 0 0 1 0 1 1 1 0 0 0 0 1 0 0 0 2 1 1 1 1 1 0 0 1 0 0 0 1 1 1 1 1 2 0 1 0 1 0 0 1 0 2 0 0 0 1 1 1 1 1 0 0 2 0 1 1 1 1 2 1 1 1 1
13 | 0 1 1 2 0 1 1 0 2 2 1 1 0 1 1 0 2 1 2 1 1 1 0 0 0 1 1 2 1 0 1 0 0 0 1 1 1 0 0 2 1 0 0 2 2 1 1 1 0 1 1 0 0 1 1 0 0 1 2 0 0 1 1 0 0 1 1 1 0 0 2 1 2 1 2 1 0 0 0 1 0 1 0 0 2 1 0 1 0 0 0 0 0 0 1 1 1 1 0 0 1
14 | 1 0 1 0 1 1 1 0 1 1 0 0 2 0 0 1 0 0 0 0 2 0 0 1 1 2 1 1 0 1 0 1 2 0 1 0 1 1 1 2 1 0 1 0 1 1 0 0 2 0 0 1 1 0 2 1 1 0 0 1 1 1 0 0 0 1 1 0 0 2 0 1 0 1 0 1 2 1 0 0 1 1 2 0 2 0 0 0 1 0 1 0 2 1 1 1 1 1 1 0 1
15 | 0 1 0 0 0 0 0 1 1 1 0 0 1 1 1 1 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 0 1 0 0 0 0 1 1 0 0 0 0 1 0 1 0 1 0 1 1 0 0 2 0 0 1 2 0 2 1 0 1 1 0 1 1 1 0 1 0 1 1 1 0 1 1 1 1 0 0 0 0 1
16 | 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 1 2 0 0 1 0 0 1 0 0 0 0 0 0 1 2 1 1 1 1 0 1 0 0 2 1 1 0 1 1 1 1 1 2 1 1 1 0 0 0 1 0 0 0 1 1 0 1 1 2 1 1 0 1 2 1 0 0 1 2 1 1 0 0 0 1 0 0 1 0 1 2 1 1 0 0 0 1 0 0 0 1 1 0 1 1
17 | 0 0 0 0 0 2 1 0 0 1 1 0 1 0 2 1 0 0 1 0 0 1 0 0 0 0 0 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 0 2 1 1 0 1 2 1 0 1 1 1 0 1 1 0 0 1 0 1 0 1 2 1 0 1 0 0 2 2 0 1 1 0 1 0 1 0 0 0 1 0 1 0 1 1 0 1 0 0 1 0 0 0 1 2 0 1
18 | 0 0 1 1 0 2 0 0 0 0 1 1 0 1 0 0 1 0 1 1 0 0 2 0 1 1 0 1 0 1 0 1 0 0 0 0 1 0 1 1 1 2 1 0 1 0 1 1 0 1 0 1 0 1 0 0 1 1 1 1 0 0 0 0 1 1 0 1 1 1 1 1 0 0 0 2 1 1 0 1 0 1 1 1 0 0 0 1 1 0 1 1 1 2 0 0 0 0 1 1 1
19 | 2 0 1 0 1 2 0 0 0 2 1 0 2 1 1 1 0 1 0 1 0 0 2 0 0 1 0 0 0 0 1 1 0 1 0 1 0 0 2 1 0 2 0 1 1 0 1 1 0 1 0 2 1 1 0 1 1 0 1 0 0 0 2 1 1 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 1 0 0 0 0 0 1 2 0 0 0 2 1 1 0 0 1 1 1 0 1
20 | 1 1 1 0 0 1 0 1 1 1 0 1 2 1 1 1 1 1 1 0 0 1 1 1 1 0 0 0 0 1 2 0 1 0 1 1 1 1 1 0 0 1 2 1 0 1 1 0 0 0 0 0 2 1 1 1 1 1 0 1 2 0 0 0 0 1 1 2 1 1 0 0 0 0 0 1 0 0 1 1 1 1 0 1 2 0 0 1 0 1 1 0 1 0 1 0 0 0 2 1 1
21 | 1 0 0 0 0 0 1 0 1 1 1 1 0 1 2 0 0 1 0 1 1 1 1 0 1 1 0 1 0 1 1 1 0 0 1 1 0 2 0 0 1 2 0 0 2 1 0 0 1 0 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 1 0 0 2 1 2 1 0 1 0 0 0 1 1 1 1 0 0 1 1 1 0 0 0 0 0 1 1 2 0 0 0 0 0 0 1
22 | 0 0 0 0 1 0 1 2 1 1 0 1 1 1 1 0 0 1 0 0 2 0 1 0 1 1 0 1 0 1 0 1 2 1 0 0 0 1 0 1 0 0 1 0 0 0 1 1 1 2 0 0 0 2 1 0 1 2 0 0 0 0 0 1 0 0 0 1 0 1 0 0 0 1 0 0 1 0 0 0 1 0 0 2 0 0 1 0 0 0 0 0 1 0 0 2 1 0 1 0 1
23 | 0 0 1 0 0 0 1 0 1 0 1 0 1 1 2 0 1 1 0 1 0 2 0 1 0 0 1 1 1 0 0 0 0 0 0 1 1 1 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 2 2 1 1 0 2 0 0 1 0 0 0 0 1 1 0 1 0 0 0 1 1 0 1 1 1 0 0 0 1 1 0 1 1 1 1 0 1 1 1 1 1 1 0 0 1 1 1
24 | 0 0 1 2 0 1 0 2 0 1 1 0 1 1 1 0 1 1 1 1 0 0 2 1 0 0 0 0 1 1 1 1 1 1 0 1 1 1 1 0 0 1 1 0 1 1 1 1 1 1 2 0 1 2 1 0 1 0 0 1 1 0 0 1 0 0 1 0 2 0 0 0 0 0 0 2 0 1 1 0 1 1 0 1 1 1 0 0 1 1 0 1 0 1 1 0 0 2 0 0 1
25 | 1 1 0 2 0 2 2 1 1 1 1 0 1 1 0 1 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 1 0 0 1 0 1 0 0 1 2 1 0 0 1 1 0 0 1 1 1 0 1 2 0 0 2 0 1 1 0 0 0 0 1 0 1 2 2 0 0 2 1 1 1 1 1 1 0 1 0 0 1 1 1 0 0 1 1 1 1 0 1 1 0 1 1 1 1 1 1
26 | 0 1 1 1 1 0 0 1 0 1 2 1 0 2 1 0 0 1 1 2 2 0 1 1 1 0 0 0 1 1 0 0 1 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1 1 0 0 1 1 0 0 1 1 1 2 1 1 1 1 1 1 2 1 2 0 1 0 0 0 0 0 1 2 0 1 0 1 0 2 0 0 1 0 1 0 0 0 0 1 0 2 1 1 1 2 1 1
27 | 0 1 1 1 1 2 0 0 0 0 1 0 1 0 0 0 0 1 0 2 0 0 0 0 2 1 1 0 0 0 0 2 0 0 2 0 0 1 0 0 1 0 0 1 1 0 0 0 1 0 1 0 0 0 0 2 0 0 0 0 0 0 0 0 0 1 0 1 1 1 1 1 2 0 0 1 1 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0 0 1 0 1 1 1
28 | 1 1 2 0 0 0 1 1 1 1 1 0 0 1 0 1 1 1 1 0 0 0 0 0 1 0 1 1 0 1 0 2 0 1 0 1 0 1 0 1 1 1 1 0 1 0 1 0 0 2 0 1 1 1 2 1 1 1 1 1 0 0 2 0 0 0 1 0 0 0 0 1 0 2 0 0 1 2 1 0 1 2 0 1 0 0 0 1 1 1 0 0 1 1 1 2 1 2 1 1 1
29 | 0 0 0 0 2 0 2 0 0 2 1 1 1 0 0 0 1 0 0 1 0 0 1 1 1 1 2 0 1 0 2 0 1 1 0 1 1 0 0 0 0 0 1 1 1 1 0 2 2 1 1 0 1 1 1 0 0 1 0 0 0 1 1 0 1 2 0 1 2 1 1 0 1 0 0 1 1 0 2 0 1 0 0 1 0 0 2 1 0 0 0 0 2 1 0 2 0 1 0 2 1
30 | 1 0 0 0 1 1 0 2 0 0 0 0 1 0 1 0 1 0 1 1 0 0 1 0 1 0 1 1 1 0 1 0 2 0 1 1 1 0 1 1 1 0 0 0 0 1 2 1 1 0 0 1 1 1 1 0 1 1 1 2 0 1 0 1 0 0 2 1 0 0 0 1 0 0 1 2 1 0 1 0 0 1 0 0 2 0 2 1 1 1 0 0 0 0 2 0 0 0 0 0 1
31 | 0 1 0 1 1 0 1 1 1 1 0 0 0 0 1 1 1 2 2 0 0 0 1 0 0 0 2 0 0 1 0 1 1 1 1 1 0 1 1 1 1 0 0 0 1 0 0 0 0 0 2 0 1 0 0 0 0 1 1 1 1 0 0 1 1 1 0 0 1 1 1 0 0 0 1 2 0 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 0 0 1 1 0 0 1 1 1
32 | 1 2 1 1 0 1 1 0 2 2 1 2 0 0 1 0 0 2 0 0 0 0 1 1 2 0 0 0 1 0 0 0 1 0 1 1 2 0 0 1 1 2 0 1 1 0 2 1 1 0 1 0 0 1 1 1 0 0 1 0 0 1 1 0 0 1 0 1 2 2 1 0 1 1 0 1 0 1 1 1 1 1 0 0 1 1 0 1 2 0 1 2 1 1 2 1 0 0 1 0 1
33 | 0 0 1 1 1 0 1 1 0 2 0 1 1 2 1 0 0 2 0 0 1 0 1 0 0 0 0 0 2 1 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 1 2 0 1 1 1 1 1 0 0 0 0 1 0 1 0 1 0 1 1 0 0 1 0 1 0 0 0 1 1 1 1 0 0 0 0 0 1 1
34 | 0 1 1 0 0 1 2 0 0 1 0 1 1 1 0 1 1 1 0 1 0 2 0 1 1 2 0 0 0 0 0 2 0 0 0 1 0 1 1 0 2 1 0 2 1 0 0 0 1 0 2 0 1 2 0 0 1 1 1 0 1 0 0 1 0 0 2 2 1 0 1 1 1 1 1 0 1 2 1 1 2 1 2 0 0 0 0 1 1 0 1 1 0 1 0 0 0 0 1 0 1
35 | 1 1 0 1 1 1 0 0 0 2 0 1 0 2 1 0 0 0 1 1 1 1 2 0 1 1 0 0 1 0 1 1 1 0 0 0 0 0 0 0 1 0 1 0 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 1 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 0 1 0 0 0 1 1 0 0 1 1 0 0 2 0 0 1 1 0 1 1 0 0 0 0 1
36 | 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 0 0 1 0 0 1 1 2 0 1 1 2 1 0 2 1 1 1 1 1 0 1 1 0 0 1 1 1 1 0 0 0 1 0 1 0 1 0 0 1 1 1 0 0 0 0 1 0 0 1 1 1 0 0 0 2 1 0 2 2 0 2 2 0 1 1 1 0 0 1 1 0 1 1 0 1 0 1 1 1 0 1 1 0 1 1
37 | 0 1 1 0 1 1 0 1 1 1 1 0 0 1 1 0 1 0 0 2 0 1 0 0 1 0 1 1 0 2 2 0 1 1 0 0 0 0 0 0 1 0 1 1 1 2 1 0 1 1 1 1 0 1 0 0 1 2 1 1 1 0 1 1 0 1 1 1 0 1 0 1 1 0 0 0 0 0 1 2 0 0 0 0 2 1 1 0 0 0 0 1 1 1 1 1 1 2 0 0 1
38 | 0 0 1 0 2 1 0 1 0 1 0 1 0 0 1 1 0 0 0 0 1 0 1 2 1 0 0 0 0 0 0 0 0 0 0 1 1 1 0 1 1 0 1 1 1 1 0 1 0 0 1 0 1 0 2 0 0 1 1 0 0 0 0 1 0 2 0 0 0 0 1 1 0 0 2 2 1 2 0 0 0 0 0 0 0 1 1 0 0 1 0 0 0 0 1 2 1 1 1 1 1
39 | 0 1 0 1 0 0 0 0 2 0 1 1 2 1 1 1 1 0 1 1 0 0 1 1 0 1 1 1 1 0 1 0 1 0 0 1 1 0 0 0 1 1 1 2 1 1 0 1 0 0 0 1 0 0 2 1 0 0 0 0 0 0 1 2 2 1 0 1 1 1 1 0 1 1 2 1 1 1 0 0 0 1 1 1 0 0 1 1 1 1 0 1 0 0 1 1 0 2 2 1 1
40 | 0 0 1 1 1 0 0 1 0 1 0 1 0 1 2 1 1 1 0 1 1 2 1 0 0 1 0 0 1 0 0 1 1 0 0 2 0 1 2 0 0 1 1 0 1 1 1 1 0 0 1 0 2 0 1 0 1 2 0 0 0 0 0 1 1 1 0 0 1 1 0 0 0 0 2 0 0 0 0 1 1 0 0 0 1 2 0 0 1 1 0 2 0 0 1 0 1 2 0 1 1
41 | 1 2 0 0 0 0 1 0 1 1 2 1 1 0 0 1 1 1 1 0 1 0 1 1 0 0 0 0 0 2 0 0 0 1 0 2 1 1 1 0 0 1 1 1 0 0 0 2 1 2 0 0 0 0 1 1 1 0 1 1 0 0 0 1 0 1 1 0 1 0 1 0 1 2 1 0 0 1 0 1 1 1 1 1 2 1 2 1 2 0 0 1 0 0 1 0 1 1 1 2 1
42 | 1 0 0 1 0 0 1 1 2 2 0 1 1 0 0 0 1 1 0 0 1 2 0 0 0 1 2 1 0 1 1 1 1 0 1 1 1 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 0 1 0 1 1 1 0 1 0 0 0 1 1 0 1 1 0 0 2 0 1 1 1 1 0 0 0 0 1 1 0 0 0 1 0 0 1 1 2 1 0 1 0 0 0 0 0 1 1
43 | 0 0 0 1 1 0 1 0 0 0 0 0 1 0 0 1 1 0 0 1 2 1 1 0 0 1 0 0 0 2 1 0 1 0 1 2 0 0 2 1 0 0 1 2 1 1 0 0 0 0 0 1 1 1 0 0 1 1 1 0 2 0 1 0 1 1 0 1 1 1 0 0 1 0 0 0 0 2 0 2 2 1 0 1 1 0 0 0 0 1 0 1 1 1 0 0 0 0 1 1 1
44 | 0 0 0 0 0 0 1 0 0 1 2 1 1 0 1 0 1 1 1 0 1 1 1 1 1 0 0 1 0 0 1 0 0 1 1 0 1 0 0 1 1 1 0 0 0 0 1 0 1 1 0 2 1 2 1 0 1 0 1 0 2 0 0 1 1 0 1 0 1 2 1 1 0 0 0 1 0 0 1 0 0 2 2 0 0 0 1 1 0 0 0 1 1 1 0 0 0 1 1 0 1
45 | 1 1 1 0 1 0 2 2 1 0 0 0 2 0 0 1 1 1 0 1 1 0 0 2 0 0 1 1 0 0 0 0 1 0 0 1 1 1 1 0 1 0 0 1 1 2 1 0 0 1 1 1 1 1 1 0 0 1 0 1 1 0 1 1 2 1 1 1 0 1 0 2 0 0 0 1 1 0 1 0 0 0 1 0 0 1 0 0 0 2 0 1 0 1 0 0 0 0 1 1 1
46 | 1 0 0 0 1 0 0 0 0 0 0 0 1 1 0 0 0 0 2 0 0 0 0 0 1 1 0 1 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 2 1 1 1 0 0 0 1 1 0 0 2 0 1 0 0 0 1 2 1 1 2 1 1 1 0 1 0 2 1 0 1 1 1 1 0 0 0 1 1 0 1 1 0 1 1 0 1 0 0 2 1 1 1 1 1 1
47 | 2 0 0 0 0 0 0 1 0 0 0 2 0 0 2 1 1 0 0 2 1 1 0 0 0 0 0 1 1 0 1 0 1 0 1 1 2 0 2 0 0 2 1 1 0 2 0 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1 1 1 1 1 0 1 1 0 1 1 2 0 1 0 0 0 1 0 0 1 0 2 0 0 0 1 0 0 0 0 0 0 0 0 1 2 0 0 1
48 | 0 1 0 0 0 1 0 0 2 1 0 0 0 1 0 0 0 0 0 1 0 1 1 1 2 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 2 2 0 0 0 1 0 0 0 0 0 0 0 0 2 0 0 1 1 1 0 0 2 2 1 0 0 1 1 0 0 0 0 0 2 1 1 1 2 1 0 1 1 1 0 1 1
49 | 1 1 0 0 1 1 0 0 1 1 0 0 1 0 1 1 1 0 0 2 1 1 0 0 0 0 0 1 1 1 2 0 0 0 2 0 2 1 1 0 0 1 2 0 1 0 2 1 0 1 0 1 0 1 1 0 0 0 1 1 1 0 1 1 1 2 2 0 2 0 2 1 0 1 0 0 1 0 1 0 0 0 0 1 2 0 1 0 0 0 2 0 1 1 2 1 1 1 1 1 1
50 | 0 0 0 0 0 1 1 1 1 1 0 0 0 0 0 2 1 0 0 2 2 0 1 1 0 2 2 0 0 1 0 2 0 1 2 0 0 0 0 1 0 1 1 0 1 0 1 1 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 1 2 1 0 0 0 0 1 0 0 0 0 0 0 2 0 1 1 1 0 0 2 0 1 1 0 0 0 1 0 0 1 1 1 1 0 2 1
51 | 0 0 0 0 0 1 0 1 0 1 1 0 1 0 1 0 0 1 1 2 0 0 0 0 1 1 1 0 0 1 1 1 0 1 1 0 0 1 1 2 0 0 1 0 0 0 0 1 1 0 0 1 0 0 1 0 1 0 0 0 1 1 0 0 0 1 0 1 1 1 1 1 1 0 0 0 2 1 2 0 1 1 1 0 1 1 1 2 0 0 2 0 2 1 2 1 0 0 0 1 1
52 | 1 0 0 0 2 0 0 0 1 1 1 2 1 1 0 1 0 0 1 0 2 0 0 0 0 0 1 0 2 0 0 1 1 1 1 0 0 1 1 0 1 0 0 0 0 0 0 0 0 1 0 1 1 1 0 1 1 0 0 2 1 1 2 2 1 1 2 0 1 0 1 0 1 0 1 2 1 1 0 1 0 0 0 0 1 0 0 1 0 1 2 0 1 2 0 0 0 0 0 1 1
53 | 0 1 2 0 0 0 0 2 1 0 0 0 1 1 0 1 1 2 0 0 1 1 0 0 0 1 0 0 0 0 0 2 1 0 0 0 2 0 2 1 2 0 1 0 0 0 1 2 0 1 0 2 0 0 1 0 0 0 0 0 1 0 0 1 0 1 0 2 1 0 0 2 0 0 0 1 0 2 1 0 0 0 1 1 1 2 0 0 0 0 2 0 1 1 1 1 0 1 2 0 1
54 | 0 0 0 1 1 1 1 0 0 0 1 1 0 1 0 0 1 1 0 0 1 0 1 0 0 2 0 0 1 1 0 0 2 1 1 1 0 0 1 0 0 0 1 1 1 0 0 0 0 1 0 0 1 1 2 1 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 1 0 1 1 1 1 0 1 0 0 1 0 0 1 1 0 1 1 0 1 0 2 0 1 0 0 0 1 0 1
55 | 1 1 1 0 0 1 0 1 0 0 2 0 0 0 0 0 0 1 0 1 0 0 2 1 1 0 1 0 0 0 0 0 1 0 1 1 1 0 0 1 1 1 2 1 0 0 0 2 1 0 2 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 1 1 0 0 1 2 1 1 0 1 0 1 1 1 1 1 0 1 1 0 1 0 2 1 0 0 2 1 0 0 1
56 | 1 2 0 1 1 0 1 1 0 0 0 0 1 0 1 0 2 0 1 1 2 1 0 0 0 0 2 0 0 0 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 1 1 0 0 0 1 0 0 0 2 2 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 1 1 1 1 1 0 0 0 1 0 1 1 1 1 1 0 0 0 0 1
57 | 0 0 0 1 0 0 1 0 1 0 0 0 1 1 1 2 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 1 0 0 1 1 1 1 0 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 0 0 0 1 0 1 1 0 0 1 1 2 0 0 0 0 1 0 0 0 0 2 0 0 1 2 1 1 0 1 1 1 2 1 0 1 0 0 0 0 2 1 0 0 1
58 | 0 0 1 0 1 0 2 0 1 1 0 0 0 0 1 0 1 0 0 0 1 1 2 0 0 0 0 1 0 0 0 1 1 2 0 0 2 1 1 2 1 0 0 0 1 0 1 0 1 0 1 0 0 0 0 1 0 1 0 0 0 1 1 1 1 1 0 0 0 0 0 0 0 1 1 2 2 1 0 1 0 0 2 2 1 2 0 0 0 0 2 1 0 0 0 0 0 2 0 0 1
59 | 2 0 0 1 0 1 1 2 1 0 2 0 0 0 1 1 1 1 0 1 1 1 0 0 1 2 0 1 0 1 0 0 0 1 0 0 1 1 0 0 0 0 0 0 0 0 1 1 2 0 0 0 1 2 2 1 0 0 0 1 1 1 0 0 0 1 1 0 0 1 1 1 1 1 1 1 1 0 2 0 1 0 2 2 1 1 1 0 0 1 1 0 1 2 1 1 0 0 1 1 1
60 | 1 1 0 0 1 0 0 0 2 0 0 2 0 1 0 0 1 0 0 0 0 1 0 0 0 1 1 1 0 0 0 0 0 1 1 0 2 0 2 0 1 0 1 1 1 1 1 0 0 0 1 0 2 2 1 0 1 1 1 0 1 1 0 2 0 0 1 0 1 0 0 1 0 0 0 1 0 1 1 2 1 0 1 0 0 0 0 1 1 2 0 2 1 0 1 0 0 0 0 1 1
61 | 0 0 1 1 1 1 1 0 0 1 0 1 1 1 1 0 1 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 2 2 0 1 1 0 2 0 1 1 2 0 1 0 1 1 0 2 0 1 0 0 0 1 0 1 0 1 1 1 0 0 1 0 2 1 0 1 0 1 1 2 1 0 2 2 0 0 0 1 0 1 0 2 1 0 0 1 2 0 0 0 1 1 0 1 1 1
62 | 0 1 1 2 1 0 0 1 1 0 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 0 2 1 1 1 0 0 1 1 2 0 0 1 1 1 1 2 1 2 0 1 0 0 0 2 0 1 0 0 0 0 0 1 1 1 1 0 1 0 1 1 1 0 1 1 0 1 1 1 1 0 0 1 1 0 2 0 2 1 2 0 1 1 0 0 0 0 1 1 0 2 0 1 0 0 1
63 | 0 0 1 0 1 1 0 2 1 1 1 1 0 1 0 1 1 0 0 0 0 1 2 0 1 0 0 1 1 2 1 0 1 1 1 2 0 1 2 1 0 1 0 0 0 0 1 1 0 1 0 0 2 1 1 1 1 0 0 0 2 1 0 1 1 0 2 1 0 0 0 0 0 1 1 0 2 1 0 1 1 0 0 0 0 1 0 1 0 0 0 1 0 0 1 1 2 0 1 1 1
64 | 0 0 0 1 0 0 0 2 2 1 1 1 0 0 1 1 0 1 1 1 1 0 0 1 1 1 0 0 1 0 2 1 1 0 0 0 0 1 0 1 0 0 1 0 1 0 0 1 1 0 1 0 0 1 0 0 0 1 1 1 2 0 0 0 1 1 0 1 0 1 2 1 0 1 0 0 1 2 0 0 1 0 1 0 0 0 0 1 0 1 1 1 1 0 1 1 2 1 1 1 1
65 | 1 0 0 0 0 0 1 2 0 1 1 0 0 0 0 0 0 2 2 1 1 0 1 2 0 1 0 1 0 0 1 0 1 0 1 1 0 1 0 0 2 0 1 0 0 1 0 0 0 1 1 0 0 1 1 2 1 0 1 0 0 1 0 1 1 1 1 0 1 2 0 2 1 1 0 1 0 2 1 0 1 0 0 0 1 0 1 0 1 0 0 2 1 2 0 0 1 0 2 1 1
66 | 1 1 1 1 1 2 0 1 2 0 0 0 1 2 1 2 2 1 0 2 0 2 0 0 1 0 1 1 1 0 0 0 0 2 0 0 1 0 1 0 0 0 1 0 1 0 1 0 1 0 1 0 1 2 1 0 1 1 0 1 2 1 1 1 0 2 1 0 0 1 1 0 1 0 2 1 0 1 0 0 0 1 1 1 0 1 1 0 0 1 1 0 1 2 1 1 1 1 0 0 1
67 | 0 0 2 0 1 1 1 0 1 0 0 1 1 0 1 1 1 0 2 0 0 0 1 0 0 1 0 1 0 0 0 2 1 0 1 0 2 0 1 1 1 0 1 0 0 1 1 1 1 0 1 1 1 2 1 0 1 0 0 0 1 0 0 0 0 0 0 0 2 1 1 1 1 0 0 1 1 1 1 0 1 0 1 1 1 1 2 1 1 1 0 0 1 1 0 2 2 1 0 0 1
68 | 0 1 1 0 1 1 0 1 2 0 1 2 0 2 2 1 0 1 0 0 1 2 0 1 0 1 1 0 1 1 0 0 0 1 0 1 1 0 1 1 0 0 0 1 0 0 1 0 1 0 0 0 1 0 0 1 1 1 0 0 1 1 1 0 1 1 1 2 0 1 0 0 1 0 0 0 0 1 0 1 1 1 2 1 1 2 0 0 1 0 0 1 0 1 1 1 1 1 2 1 1
69 | 1 0 2 0 2 1 0 0 1 1 1 1 0 1 0 1 1 0 0 1 0 0 0 1 1 0 0 2 0 0 1 1 0 0 0 0 0 0 1 0 2 0 1 0 0 0 0 0 0 1 1 0 1 1 0 0 1 1 1 0 0 1 1 0 0 0 1 0 1 1 0 0 1 1 0 2 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 1 0 0 0 0 0 1 0 1
70 | 1 0 0 0 0 1 1 0 1 1 1 2 1 1 2 0 0 0 1 0 0 0 0 1 1 0 1 1 1 1 1 1 1 1 0 2 0 2 2 0 1 0 0 0 1 1 1 0 1 1 0 0 0 1 1 1 0 0 1 1 0 2 2 0 2 1 0 0 0 0 1 1 0 0 1 2 0 1 1 1 0 1 1 0 0 0 0 0 1 1 1 0 1 1 1 0 1 1 2 1 1
71 | 2 2 0 0 2 0 0 1 1 0 0 0 1 2 0 0 2 2 0 0 0 0 1 2 0 0 0 1 0 1 0 1 1 0 0 2 0 0 1 1 0 1 1 0 1 1 1 0 0 1 2 1 0 0 0 1 1 1 1 0 1 0 0 1 1 0 1 1 0 0 0 0 0 1 2 1 1 1 1 0 1 1 2 2 1 0 1 2 0 0 0 0 1 0 0 0 0 0 0 2 1
72 | 0 1 2 1 1 0 1 1 1 0 0 1 1 2 0 1 0 1 1 0 0 0 2 0 0 1 0 0 0 2 0 0 0 1 1 2 0 0 1 1 0 0 0 1 1 0 1 1 0 0 1 0 0 1 1 2 0 1 1 0 0 1 0 1 1 1 1 0 1 0 0 1 1 0 1 0 1 0 0 1 1 1 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 0 2 1 1
73 | 0 0 1 0 1 0 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 1 0 1 0 1 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 1 1 1 1 0 0 0 2 1 0 1 1 1 2 0 1 0 1 1 0 1 0 0 0 0 1 0 0 2 0 0 0 1 0 1 0 0 0 0 1 2 2 0 2 0 0 1 1 0 1 1 1 0 0 0 0 1 1
74 | 1 0 0 0 1 0 1 0 0 1 0 1 1 0 0 2 0 1 1 1 1 1 1 1 1 1 0 2 0 0 0 0 0 1 0 0 2 0 0 1 1 0 1 0 0 0 1 0 1 1 1 2 1 0 0 1 0 0 0 0 1 0 1 0 0 1 1 0 0 1 0 0 0 1 0 1 0 0 1 0 1 2 0 2 0 1 0 0 0 1 0 0 1 1 1 0 0 0 1 1 1
75 | 0 1 2 2 0 0 0 1 0 1 1 0 1 1 2 1 1 0 0 2 0 1 1 1 0 0 2 0 2 1 0 2 0 1 0 1 0 0 1 0 1 0 1 1 0 1 1 0 0 0 1 1 0 0 0 2 0 1 0 0 2 0 0 1 1 0 0 0 1 1 0 0 1 1 1 0 1 2 0 0 1 1 1 0 1 0 0 1 0 0 0 1 1 1 0 0 1 1 2 1 1
76 | 1 0 0 1 0 1 0 1 0 0 0 0 0 1 0 1 0 1 1 0 0 1 1 2 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 0 0 1 1 2 1 1 1 1 0 0 2 1 1 1 0 1 1 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 0 0 1 0 1 1 0 0 1 2 1 0 0 0 1 1 1 0 1
77 | 0 0 1 1 1 2 1 0 0 0 1 0 1 1 0 0 0 1 1 0 1 0 0 0 1 2 0 1 0 0 0 2 0 0 2 0 0 0 2 1 1 2 0 1 2 1 1 0 0 0 1 0 0 0 0 1 1 1 0 1 1 0 0 0 0 2 0 0 0 0 0 1 2 1 0 1 1 1 1 1 2 1 1 1 0 1 0 2 1 2 1 1 1 1 2 0 1 1 1 0 1
78 | 1 0 0 1 0 1 2 0 1 2 0 0 1 0 0 0 0 0 0 1 0 1 1 2 2 0 0 0 0 2 1 1 0 0 1 1 2 0 0 1 1 1 0 0 1 1 1 0 1 1 0 0 1 0 0 2 0 0 0 0 0 0 1 0 0 0 0 0 0 0 1 0 1 0 0 0 0 1 0 0 1 1 1 1 0 0 1 0 1 2 1 0 1 1 2 1 1 0 0 1 1
79 | 1 1 0 1 0 0 1 1 0 0 0 1 1 2 1 0 0 0 1 1 1 0 0 2 0 0 0 1 1 1 1 2 0 0 1 0 0 0 1 1 1 0 0 1 1 0 0 0 1 1 1 0 0 2 1 0 0 0 0 1 0 1 0 0 1 1 0 0 1 1 1 0 1 0 0 0 1 0 0 0 0 2 1 1 1 0 1 0 0 1 1 0 1 2 1 1 0 0 0 0 1
80 | 1 1 0 0 1 0 0 1 0 0 0 0 0 1 1 0 1 1 1 1 1 2 0 1 1 2 1 1 1 1 0 0 1 0 1 1 0 2 2 0 1 0 1 0 0 1 0 2 0 1 2 1 1 0 1 0 1 0 0 0 0 1 1 1 0 0 1 1 0 1 0 1 2 0 1 0 0 2 0 0 0 0 2 0 0 0 1 1 0 1 0 0 0 0 2 0 1 0 0 0 1
81 | 0 0 0 1 1 0 1 0 1 0 0 1 0 0 0 2 0 0 0 0 0 0 1 1 1 0 1 1 1 2 2 0 2 1 1 0 1 2 1 0 1 2 1 0 1 2 0 0 0 2 1 0 0 1 1 0 0 1 1 1 0 1 0 1 1 1 1 1 1 0 0 1 1 0 0 1 0 1 0 0 1 1 0 1 1 0 1 2 0 1 0 1 1 2 1 1 1 1 0 1 1
82 | 0 0 0 0 0 1 0 1 1 1 0 1 1 2 0 1 0 0 1 2 1 2 1 0 0 1 1 1 1 1 0 1 1 1 0 0 2 1 2 0 0 0 0 0 0 1 1 1 0 1 0 1 1 0 0 1 1 1 2 0 1 1 1 2 0 1 1 2 1 0 2 0 0 0 2 0 0 0 0 0 0 0 0 0 2 0 1 1 0 1 1 0 1 0 0 0 2 2 0 0 1
83 | 1 0 1 1 1 1 1 1 0 0 1 1 0 0 0 1 0 0 1 0 0 1 1 0 1 1 0 0 1 0 2 0 1 1 1 0 1 0 0 0 0 1 1 0 1 0 0 0 1 1 0 1 1 1 0 2 0 0 0 2 0 0 0 1 0 2 0 1 1 0 0 0 0 1 0 2 0 1 1 1 1 2 0 1 1 1 0 1 0 0 2 0 1 0 0 1 0 0 0 0 1
84 | 0 1 0 0 1 1 0 0 1 0 1 1 0 0 2 1 1 0 1 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 1 0 0 0 0 0 2 2 0 0 2 0 1 1 0 0 1 1 0 2 0 0 0 0 1 0 0 1 1 1 0 1 1 0 1 0 1 1 1 0 2 1 1 0 2 0 1 0 0 1 1 1 1 1 1 1 0 0 0 1 1 1 0 0 1 1 1
85 | 0 0 1 0 1 1 0 2 0 0 1 1 1 0 0 1 0 1 1 1 1 1 2 0 2 0 0 0 0 0 0 1 0 1 2 0 1 0 1 1 1 0 0 0 1 1 0 2 0 0 1 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 1 0 0 1 1 0 0 1 1 1 0 1 1 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 1 1 0 0 1 0 1
86 | 1 0 2 0 1 1 1 1 1 1 2 1 0 0 0 0 0 1 0 1 0 1 1 0 1 0 1 1 2 0 2 0 1 2 0 0 0 2 1 0 0 0 1 1 1 1 0 1 0 0 0 0 1 0 0 0 2 1 0 1 1 1 1 1 0 0 0 1 1 0 0 0 1 1 0 2 0 0 2 1 0 0 0 0 2 0 0 1 2 1 1 1 0 1 0 0 1 0 0 1 1
87 | 1 0 1 0 1 0 1 1 1 1 0 0 1 0 0 1 0 2 2 0 1 1 0 0 0 0 1 2 0 1 1 1 1 1 0 1 0 1 0 0 0 0 2 0 1 0 1 0 0 0 0 1 2 1 1 0 0 1 0 0 0 2 0 1 1 1 1 0 0 0 1 0 1 0 0 2 0 0 0 0 0 1 1 1 0 1 2 1 1 0 0 1 0 1 1 1 1 1 0 0 1
88 | 0 1 1 0 1 2 1 1 0 2 2 1 1 1 0 1 1 1 2 1 1 0 1 1 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 0 0 1 1 0 1 0 1 0 1 0 0 0 0 1 1 1 0 0 0 1 1 1 1 1 1 0 2 1 0 0 2 0 0 1 1 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 2 0 0 0 0 1 0 1 0 0 1
89 | 1 0 1 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 0 1 0 1 0 0 0 0 2 2 0 0 0 2 0 0 2 1 0 1 0 1 1 0 0 0 0 1 1 1 1 1 0 1 0 0 1 0 0 0 1 0 1 2 0 0 0 1 0 0 0 2 2 0 1 0 0 0 1 2 1 1 0 0 0 0 1 0 0 1 0 1 1 0 1 2 0 0 0 1 0 1 1
90 | 1 1 1 0 0 2 0 0 1 0 2 1 0 0 1 0 0 0 1 0 0 0 1 1 1 0 1 0 2 0 2 1 1 1 1 0 2 0 1 1 2 1 0 0 1 0 0 1 1 0 0 0 0 0 1 0 1 1 0 2 0 1 1 1 0 1 0 0 2 0 0 1 0 1 0 0 0 2 1 1 0 1 0 1 1 0 0 0 1 0 1 0 1 1 1 1 2 0 1 0 1
91 | 0 1 1 0 1 0 0 0 0 1 1 1 1 0 0 2 0 0 0 1 1 1 0 1 1 0 0 0 1 0 1 1 1 0 1 1 0 0 1 0 0 1 2 2 0 1 0 1 1 0 1 1 0 0 2 0 1 0 0 0 1 1 1 2 1 1 1 1 0 1 0 0 1 0 1 0 0 0 1 1 1 0 1 0 0 0 0 1 1 0 0 0 0 2 1 0 1 0 1 0 1
92 | 1 1 0 1 1 0 2 0 0 1 1 1 1 2 1 1 0 1 0 2 0 0 0 1 0 2 0 1 1 0 1 0 0 0 0 1 1 2 0 1 0 0 0 1 1 1 0 0 1 1 0 0 1 1 1 1 1 0 0 0 0 2 1 0 1 1 0 1 1 1 1 1 0 0 0 1 0 0 0 0 0 0 0 0 1 2 1 1 0 1 1 1 1 1 1 1 0 1 0 0 1
93 | 0 2 0 0 0 1 1 1 1 0 0 0 1 2 2 1 1 1 1 1 0 0 0 1 1 0 0 1 1 1 2 0 0 0 0 1 0 0 1 1 2 2 0 1 1 1 2 0 1 0 0 1 0 0 1 0 0 2 0 1 0 0 1 0 1 1 0 0 0 0 0 0 2 2 0 0 1 1 0 1 0 0 1 0 2 0 0 1 0 1 1 0 0 1 2 0 0 0 1 1 1
94 | 0 1 1 1 0 0 0 0 2 1 0 0 0 1 1 0 1 0 0 0 0 1 0 2 2 0 1 0 0 1 1 0 0 0 0 0 0 0 1 0 2 0 1 1 2 0 1 0 0 0 0 0 0 0 1 1 0 0 1 1 1 1 0 1 0 0 2 1 0 1 0 0 2 1 1 1 1 0 0 2 0 0 1 0 1 0 1 1 0 0 0 1 0 0 0 0 1 0 2 1 1
95 | 0 0 1 1 0 1 1 1 0 2 1 2 0 0 2 1 0 1 2 1 1 0 0 1 0 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 1 1 0 0 1 1 0 1 0 1 0 1 1 1 0 1 1 1 1 1 1 1 1 0 2 1 1 0 1 0 1 0 0 0 1 0 1 1 1 1 0 0 1 1 0 1 0 1 0 1 0 0 1 0 1 1 1 1 1
96 | 2 1 1 1 0 2 1 0 1 1 1 2 0 0 0 1 1 2 0 0 0 0 0 0 1 0 0 2 0 1 0 1 0 1 1 0 0 0 0 1 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 2 1 0 1 1 0 0 1 2 0 0 0 1 0 0 1 0 2 1 1 1 1 1 1 0 1 0 0 0 1 0 2 1 0 1 1 2 0 0 1 2 0 1 1 0 1
97 | 0 0 1 1 0 0 1 0 1 0 0 0 1 1 1 0 0 1 0 0 0 1 1 0 1 1 1 0 1 0 1 0 0 0 0 1 1 1 0 1 1 1 0 1 2 1 0 0 2 0 0 1 0 0 0 2 2 2 1 0 1 0 1 1 0 1 0 0 0 1 1 1 1 1 1 0 0 0 0 1 1 0 1 0 0 0 1 1 0 1 0 1 0 0 1 1 0 0 0 0 1
98 | 1 1 0 0 0 1 1 0 1 0 0 1 1 1 1 1 0 0 1 0 0 1 0 0 0 2 1 0 1 1 0 1 0 2 0 0 0 0 0 2 1 0 0 1 0 1 0 1 0 1 0 0 1 2 0 0 0 1 0 0 1 0 1 1 1 1 1 0 0 1 1 1 0 1 0 0 2 1 0 0 0 0 0 1 2 0 1 0 0 1 0 1 0 0 2 1 1 1 0 0 1
99 | 1 1 0 1 0 0 2 1 0 0 0 0 1 1 1 0 2 0 1 1 0 0 0 1 1 0 1 1 0 0 1 1 1 1 2 1 0 1 0 1 2 2 1 1 1 1 1 0 1 0 0 2 0 1 1 0 0 0 0 2 1 2 0 0 1 0 1 2 1 0 1 0 0 1 1 0 0 0 1 1 0 1 1 1 1 1 0 0 1 0 0 1 0 0 0 1 0 0 0 2 1
100 | 0 2 1 2 1 0 0 1 0 1 1 1 1 0 0 0 1 0 0 2 1 0 1 0 1 1 1 0 0 1 1 0 0 1 2 0 1 0 0 1 1 0 2 1 0 0 0 1 1 0 0 1 1 1 1 1 2 2 0 0 0 1 0 0 1 1 1 2 1 0 1 0 1 1 0 0 1 0 1 2 0 0 1 1 1 0 0 0 0 0 0 1 0 0 0 0 0 0 1 1 1
101 | 2 0 0 1 0 0 0 2 0 2 0 1 0 0 1 2 1 0 2 1 0 2 1 1 0 1 1 2 1 0 0 1 0 0 0 1 1 0 1 1 0 0 2 0 0 2 1 0 0 1 2 0 0 2 0 1 1 2 1 0 1 0 1 1 1 1 0 0 0 0 0 1 0 1 1 2 0 1 1 1 0 1 0 2 0 2 0 1 0 0 1 1 1 1 0 2 0 1 1 2 1
102 | 1 0 0 1 0 0 1 1 0 1 0 0 0 2 1 1 0 0 0 1 0 1 0 1 1 0 1 1 1 0 1 0 1 0 2 1 0 1 1 0 1 1 0 0 2 2 0 0 1 1 1 1 0 2 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 1 0 2 0 1 2 0 1 1 1 2 0 0 0 1 1 1 1 0 2 0 1 0 1 1 0 1 0 1 0 0
103 | 0 1 1 0 0 2 0 0 2 2 1 0 1 1 1 1 0 0 1 2 1 1 0 1 2 0 0 1 1 0 0 1 2 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 2 1 0 0 0 1 1 0 2 0 0 0 0 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 1 1 1 0 0 1 1 1 1 0 0 0 0 0 1 0 2 0 1 1 2 0 0
104 | 1 1 1 0 1 0 1 0 0 0 1 0 0 1 2 1 2 0 0 0 1 0 0 1 1 1 1 1 0 0 1 0 1 0 0 0 0 0 0 1 1 0 1 0 1 2 0 1 2 1 0 0 1 1 1 1 1 0 1 1 0 0 1 0 2 0 0 1 0 1 1 1 1 0 1 2 1 1 1 1 1 0 1 1 1 2 1 1 1 1 0 0 1 0 1 0 0 0 2 0 0
105 | 1 1 2 2 1 0 0 0 0 0 1 0 1 1 0 1 1 1 0 1 2 0 0 1 2 0 0 0 1 0 2 0 2 0 0 1 1 1 1 1 0 1 1 1 0 0 2 0 1 0 1 1 1 0 1 1 0 0 0 2 0 0 0 0 1 1 1 1 1 1 1 0 2 0 0 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 2 1 0 1 0 0 0 1 0 0 0
106 | 1 0 0 1 0 1 0 1 1 0 2 1 0 0 2 0 0 0 0 1 1 0 0 0 0 1 0 0 1 0 1 1 1 1 0 0 0 0 0 1 2 0 0 1 0 0 1 1 0 1 1 2 0 0 0 0 0 1 1 0 0 0 1 0 0 2 0 0 1 1 0 1 0 0 1 0 2 0 1 0 0 1 0 1 1 0 0 1 0 2 0 0 0 0 1 0 0 1 1 0 0
107 | 1 0 1 1 1 0 0 1 0 0 2 1 2 0 0 2 0 1 0 1 0 1 0 2 0 0 0 2 0 1 0 1 2 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 2 0 0 1 0 1 1 1 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 0 1 0 1 1 0 0 1 1 1 1 1 1 1 0 2 1 0 1 0 1 1 0 0 0 0 1 0
108 | 1 0 1 1 1 1 0 1 0 1 1 1 0 1 1 0 0 1 1 0 1 2 1 1 0 1 0 2 0 1 0 1 0 1 1 0 1 0 0 0 0 2 0 0 1 2 0 1 0 1 0 1 2 0 1 0 1 0 2 1 0 0 1 0 1 0 1 0 1 0 0 1 1 0 0 1 0 1 0 0 1 0 1 0 1 0 0 0 1 0 0 2 1 1 0 1 0 1 1 1 0
109 | 1 0 0 1 1 1 0 1 0 2 0 2 2 0 1 0 0 1 1 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 2 1 2 0 0 1 0 0 1 0 1 1 0 1 1 1 0 0 0 1 1 2 0 0 0 0 0 0 0 1 0 0 0 1 1 2 0 2 0 2 0 1 0 0 0 2 1 0 0 0 1 1 0 0 1 0 1 0 1 0 1 0 0
110 | 1 0 1 0 1 0 0 0 1 0 1 1 0 1 1 0 1 0 2 0 0 0 0 1 0 1 0 1 0 0 0 1 0 0 0 1 1 1 1 0 1 0 1 1 0 1 1 1 0 1 1 0 0 0 0 0 1 0 1 1 1 2 0 1 1 0 0 1 0 2 0 1 0 0 1 2 0 1 0 1 0 0 1 0 1 0 1 0 1 0 1 0 2 0 2 0 1 2 0 0 0
111 | 1 0 1 0 0 1 1 0 1 0 0 0 1 0 0 0 0 2 0 0 1 1 1 2 1 0 0 0 0 0 1 0 0 0 1 1 0 0 0 0 1 1 0 1 0 0 1 1 2 1 0 0 0 1 1 1 0 0 0 1 2 0 0 1 0 1 1 1 0 1 0 0 1 1 1 0 0 0 0 0 2 0 1 2 1 2 0 1 1 0 1 0 0 0 1 0 0 0 0 0 0
112 | 1 0 1 0 0 1 1 0 1 1 1 1 2 0 1 0 0 1 1 1 1 1 0 0 1 0 1 0 2 1 2 1 1 2 1 0 1 1 1 1 0 2 1 1 0 2 0 2 1 1 1 2 1 0 0 0 1 0 0 1 0 0 0 1 0 0 1 0 1 0 0 1 1 0 0 0 0 1 1 0 1 1 1 2 0 1 1 1 1 0 0 2 0 2 0 1 1 1 1 0 0
113 | 2 0 1 1 1 0 0 0 0 1 1 0 0 0 0 0 0 0 1 2 0 1 2 2 1 0 1 0 0 0 0 0 1 0 2 0 1 0 0 0 2 0 2 1 0 0 0 0 1 2 0 1 0 1 0 2 0 0 1 1 1 1 0 0 2 1 1 2 2 0 1 2 1 1 0 1 0 0 1 1 1 2 0 1 0 2 0 0 0 0 1 0 0 1 0 1 0 0 0 1 0
114 | 0 1 2 0 0 1 1 1 0 1 0 0 0 0 1 2 2 0 0 0 0 1 2 0 0 0 0 0 0 1 0 0 2 0 1 1 1 1 0 1 0 0 0 2 1 0 2 0 0 1 2 0 1 0 1 0 1 0 1 0 1 0 0 2 0 0 0 2 1 0 0 1 0 2 0 1 0 0 0 0 0 1 0 1 1 0 1 1 1 0 0 0 1 0 1 0 2 0 1 0 0
115 | 0 1 1 0 1 0 1 2 1 0 1 1 0 1 1 0 0 1 0 0 1 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 1 1 1 1 1 0 0 0 1 0 1 1 0 1 0 1 1 1 1 0 0 1 1 1 1 1 0 0 0 2 0 0 0 1 1 2 1 1 2 0 0 1 1 0 1 0 1 2 1 2 0 0 1 1 0 0 1 1 0 0
116 | 0 0 1 2 0 1 0 1 0 2 1 1 1 1 1 0 0 1 2 1 0 0 1 2 1 2 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 0 0 1 0 0 0 1 1 1 1 1 1 1 2 0 1 1 2 1 2 0 2 1 1 0 0 0 1 1 0 2 0 0 1 1 0 0 1 1 0 0 1 0 0 1 0 0 1 0
117 | 1 1 0 0 0 2 0 0 1 0 1 1 1 1 1 1 1 0 0 1 1 0 0 1 0 0 0 1 1 0 1 1 2 1 0 1 0 0 1 2 0 1 1 0 0 1 1 1 2 1 1 0 1 0 2 0 0 1 0 1 2 0 0 1 0 2 1 2 1 2 0 1 0 1 0 0 0 0 0 2 1 2 1 0 1 0 2 0 0 0 1 1 0 1 0 1 1 0 0 0 0
118 | 0 1 0 0 0 1 0 1 1 1 0 0 2 0 0 0 0 0 0 1 2 2 1 0 1 1 1 0 1 0 0 0 1 0 0 0 1 2 2 1 2 1 1 0 0 1 0 0 0 1 0 1 1 0 1 1 0 0 1 0 0 1 1 1 1 0 1 1 0 0 0 0 1 1 2 0 1 1 1 1 1 1 0 1 0 0 1 0 0 0 1 1 0 1 1 1 1 0 1 0 0
119 | 2 2 2 0 0 1 0 1 1 0 1 1 1 0 1 1 0 0 1 0 0 1 0 0 1 0 0 0 1 0 0 1 0 0 1 0 1 0 0 1 0 1 0 0 0 2 1 1 0 1 1 1 0 1 1 0 1 0 0 0 1 0 1 0 1 0 0 1 1 2 0 0 0 0 1 0 1 0 1 0 1 2 0 1 0 1 0 0 0 0 1 1 0 1 0 0 1 1 1 0 0
120 | 1 1 0 1 0 1 0 0 0 0 2 0 1 2 1 0 1 1 0 0 1 1 0 1 1 0 1 1 0 2 0 1 1 0 1 0 1 0 1 1 1 0 1 0 1 1 1 1 0 1 1 1 2 1 0 0 0 1 0 0 0 1 0 0 1 1 0 1 1 1 0 0 0 0 1 1 0 0 0 0 2 1 0 1 1 2 0 2 2 1 0 1 0 2 0 1 2 0 1 1 0
121 | 1 0 1 0 1 1 1 0 0 1 1 1 0 1 1 0 1 1 2 0 0 2 1 1 2 1 2 1 0 1 1 0 1 0 1 0 0 0 1 0 1 0 0 1 0 0 0 1 0 1 0 0 0 0 2 2 1 0 0 0 1 0 0 1 0 1 1 0 1 0 0 1 1 2 0 0 1 1 1 2 2 0 0 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 1 1 0
122 | 1 1 0 0 0 2 0 1 1 1 1 0 0 0 0 1 0 1 0 1 2 1 0 1 0 0 1 0 0 1 0 1 1 2 0 0 1 1 1 0 0 2 1 0 1 1 2 2 1 1 0 1 0 0 1 0 1 1 1 1 1 1 0 1 0 2 1 1 0 0 2 0 1 2 0 1 1 0 0 0 0 1 1 2 0 0 0 0 0 1 1 1 0 1 0 1 0 0 1 0 0
123 | 0 0 0 1 2 0 1 0 0 0 1 1 0 0 1 0 0 0 0 0 1 1 1 0 0 1 0 1 1 1 0 1 0 1 0 0 0 1 2 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 2 0 1 0 0 0 0 1 1 1 1 1 0 0 1 0 1 0 0 1 2 1 0 0 0 0 1 0
124 | 0 1 1 1 0 0 0 0 1 2 1 1 1 2 1 0 0 0 2 0 1 1 0 1 0 0 0 1 1 0 0 0 1 1 1 1 0 1 0 1 0 0 0 0 0 0 1 1 0 0 2 1 0 1 1 1 2 0 1 0 0 1 0 0 0 1 1 1 0 1 0 0 1 1 0 0 0 1 1 1 2 1 0 0 2 1 1 0 0 0 0 2 1 0 0 1 0 2 0 0 0
125 | 0 0 0 0 2 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 2 1 1 1 1 0 0 1 0 0 0 0 1 1 0 0 0 0 1 1 1 2 1 1 0 1 2 0 2 0 1 0 2 0 0 2 1 1 2 0 0 0 2 1 0 2 1 0 0 1 1 2 1 1 0 0 1 2 0 0 1 0 0 0 0 0 1 1 1 0 1 0 1 0 0 0 0 1 0 0
126 | 1 0 0 0 1 1 1 1 1 0 1 1 2 1 1 1 0 0 1 2 0 1 0 2 0 1 0 2 2 0 1 1 1 1 0 0 1 2 0 0 2 0 0 0 1 1 1 1 1 2 0 0 0 0 0 1 1 0 1 1 1 2 0 2 1 0 1 0 1 0 0 1 1 2 0 0 1 0 0 0 1 0 1 1 1 0 0 2 0 1 1 0 1 0 0 1 1 1 1 0 0
127 | 0 1 1 0 1 1 1 0 1 1 0 0 0 1 1 2 1 1 1 1 1 0 1 1 0 0 0 1 1 1 1 2 0 0 2 2 0 0 2 0 1 0 1 1 1 0 2 2 0 1 1 1 1 1 0 0 1 0 1 1 0 1 2 1 0 1 0 1 0 0 1 1 1 0 0 1 0 0 1 0 1 0 0 0 1 1 1 0 0 1 0 0 0 2 0 0 1 0 1 0 0
128 | 1 0 0 0 0 1 0 0 1 0 0 0 0 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 1 1 2 1 1 0 0 0 1 0 1 1 0 0 1 0 0 1 1 0 0 1 1 0 1 1 1 0 1 0 1 2 1 0 1 0 0 1 0 0 0 0 1 0 2 1 1 0 0 1 1 0 1 0 1 0 0 0 0 0 1 0 1 1 1 0 0
129 | 1 0 0 1 0 1 0 0 2 1 0 0 2 0 0 0 0 2 0 1 0 1 1 1 1 0 1 1 1 0 0 0 2 2 1 0 0 1 0 0 1 1 2 0 1 1 1 0 0 0 0 1 1 1 1 1 1 0 2 0 0 1 1 0 2 0 0 1 0 1 0 0 0 1 0 1 0 0 1 2 1 1 0 0 1 1 0 2 1 0 0 0 0 0 1 1 1 0 0 2 0
130 | 0 0 2 0 1 0 0 0 1 1 0 1 0 1 0 0 0 0 1 0 1 1 0 0 0 0 1 1 1 0 1 1 2 0 1 0 0 1 0 0 0 1 0 0 0 1 1 1 1 1 0 0 1 0 0 0 0 1 0 0 0 1 0 1 1 1 2 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 2 0 2 2 0 0 1 0 0 0 0 1 0 0 1 1 0 1 0
131 | 2 0 0 0 1 0 1 0 0 0 0 0 1 0 1 0 1 1 0 1 0 2 0 1 1 0 0 1 1 0 0 0 0 1 0 0 2 1 0 1 1 0 0 0 1 0 0 0 1 0 1 2 2 0 1 1 1 1 0 1 0 1 0 1 2 0 0 1 1 0 1 1 0 1 1 2 1 0 2 0 0 0 0 0 1 1 1 0 0 0 1 1 0 2 1 1 0 0 0 1 0
132 | 1 1 0 0 0 0 2 0 1 1 0 1 1 0 2 0 1 1 1 0 1 1 2 0 1 0 0 0 0 1 0 1 1 1 0 1 0 1 0 1 1 2 1 1 0 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 0 1 0 1 0 0 2 1 2 0 1 2 1 2 1 1 1 0 0 0 0 1 1 2 1 1 0 0 0 1 1 0 1 2 1 0 2 0 2 0 0
133 | 1 0 0 1 0 0 1 2 0 0 0 0 0 1 0 0 1 1 2 1 0 0 1 1 0 0 1 1 1 1 0 0 0 1 0 0 0 1 0 0 2 1 0 0 1 0 0 1 1 1 0 0 1 1 0 0 0 1 1 1 0 0 2 0 0 0 0 0 1 1 0 0 1 1 0 0 0 1 0 0 0 1 0 1 0 1 2 0 1 1 0 1 1 0 1 0 1 0 1 1 0
134 | 2 2 0 1 0 1 2 0 0 0 1 0 2 0 2 0 1 0 0 1 0 1 1 1 1 1 2 1 0 1 0 1 0 1 1 0 0 1 1 0 1 0 2 1 0 0 1 1 1 0 0 1 2 0 0 0 0 0 2 0 0 1 1 1 2 2 0 1 0 0 0 1 0 1 1 1 1 1 0 0 2 1 1 0 0 1 0 2 0 0 1 1 1 1 0 1 0 1 0 1 0
135 | 1 2 1 1 0 0 1 2 1 2 1 0 0 2 0 1 1 1 0 1 0 0 1 1 0 0 0 0 0 0 1 1 2 0 1 1 0 1 1 0 2 0 1 1 0 1 1 1 2 0 1 1 2 0 2 0 2 1 0 0 0 1 0 1 1 0 2 1 1 0 0 0 0 0 0 1 1 0 2 1 1 1 1 1 0 1 1 0 0 0 0 1 1 0 2 0 2 2 1 2 0
136 | 0 1 1 1 2 0 2 0 2 0 1 1 0 0 1 1 0 1 1 0 1 1 1 0 1 1 0 1 0 0 0 0 1 0 1 1 0 1 0 0 1 0 0 2 1 2 0 1 1 2 1 0 2 0 0 0 0 0 1 0 0 1 1 1 1 2 1 0 1 0 0 1 1 1 0 0 0 0 0 1 0 0 1 2 0 0 0 1 0 0 2 0 1 1 1 1 0 1 0 0 0
137 | 1 1 1 1 1 2 1 1 1 0 1 0 1 1 1 0 0 0 2 0 0 2 2 0 2 2 1 1 1 0 2 2 1 1 1 0 1 0 1 1 0 1 1 0 0 1 0 0 1 0 1 1 1 0 0 0 1 1 0 1 1 1 0 0 0 1 2 2 0 1 1 1 0 0 0 1 1 0 0 1 1 0 0 1 1 1 0 2 2 1 2 1 1 0 1 0 1 0 1 0 0
138 | 1 1 0 1 1 0 0 0 1 0 0 0 0 0 2 0 1 0 0 1 0 0 2 0 1 0 0 2 0 1 0 0 1 1 0 1 0 0 0 0 2 1 0 0 0 1 0 0 0 1 0 0 1 1 2 1 1 0 2 1 0 0 1 0 1 1 0 0 2 0 1 1 0 0 1 0 2 0 0 2 0 1 1 0 1 0 1 1 1 2 0 1 0 1 1 0 0 1 1 0 0
139 | 1 0 1 0 2 1 0 2 0 0 0 1 2 0 1 1 0 0 1 1 2 1 2 0 0 1 0 0 2 1 0 1 0 2 1 1 1 2 0 1 2 0 0 0 0 0 2 0 2 0 1 0 2 0 0 0 0 0 0 0 1 0 1 1 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 0 0 2 1 2 0 1 0 0 0 1 1 1 0 1 0 1 1 0 1 2 0
140 | 0 0 0 0 0 0 0 1 1 1 0 0 1 2 2 2 0 0 0 1 0 0 0 1 1 2 2 1 1 1 1 2 0 0 0 1 0 1 2 1 1 1 0 1 0 0 1 0 0 1 0 2 1 0 1 0 0 0 0 0 1 0 1 0 0 0 1 1 0 1 0 1 1 1 1 0 0 1 1 1 2 0 0 2 0 1 0 2 0 0 2 1 1 1 1 0 0 0 1 2 0
141 | 0 0 0 0 2 1 1 0 2 0 1 2 1 2 2 0 1 0 1 2 0 0 0 0 0 0 1 1 1 1 0 1 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 0 0 0 0 0 0 2 1 0 1 1 1 0 0 0 0 1 0 0 2 1 0 0 0 2 0 0 1 1 0 2 1 1 1 0 1 1 1 2 1 0 1 0 1 1 2 1 0 1 0 0 1 0
142 | 1 0 1 1 0 0 0 1 0 1 1 1 0 2 2 0 0 1 0 0 2 1 0 1 0 0 0 1 1 2 1 1 1 0 1 2 0 1 1 2 1 1 1 1 1 0 1 1 1 0 0 1 0 0 0 0 1 1 0 0 2 1 0 0 0 0 1 1 1 0 1 1 1 0 0 0 1 1 0 0 0 0 0 0 0 0 1 0 0 0 0 0 1 0 1 0 0 1 0 0 0
143 | 1 1 1 1 0 0 0 1 2 0 1 0 0 0 1 0 0 1 1 1 0 0 0 2 0 1 2 1 0 0 0 1 1 0 2 1 0 1 1 1 0 2 0 0 0 1 2 1 1 0 0 1 1 1 2 2 0 0 1 0 1 1 0 0 1 1 1 1 2 0 1 1 2 2 1 0 0 1 0 0 0 0 0 0 2 2 1 1 0 1 1 1 2 0 0 0 1 2 1 2 0
144 | 1 0 2 0 1 0 1 1 1 0 1 1 0 0 0 0 0 1 0 1 0 0 2 1 1 0 1 0 0 0 1 0 0 1 0 0 0 1 0 2 0 0 0 0 1 1 0 1 1 1 1 0 2 0 0 1 1 0 0 0 0 1 0 1 1 0 1 0 2 1 1 0 0 0 0 0 0 1 0 0 1 0 0 1 0 1 1 2 0 0 1 0 0 1 0 0 0 1 1 0 0
145 | 0 2 1 1 2 0 2 1 2 0 1 1 1 1 1 0 1 1 1 0 0 0 2 0 0 0 1 0 1 0 0 2 1 0 0 1 1 0 1 0 1 1 0 1 0 1 0 0 0 0 1 0 1 1 1 0 1 2 1 0 2 1 2 1 0 2 1 0 1 0 1 1 1 0 1 1 1 0 1 0 2 1 1 1 1 0 1 0 1 1 1 0 0 0 1 0 1 0 0 1 0
146 | 0 1 0 1 1 1 0 0 0 1 1 0 0 2 0 0 0 1 0 0 1 2 1 1 0 0 0 1 1 1 1 2 0 1 0 1 1 0 0 0 0 0 0 1 0 1 0 1 0 1 1 0 1 0 1 1 1 1 1 1 1 2 0 0 1 0 0 1 2 1 0 0 0 0 0 1 1 0 1 0 1 1 2 1 0 1 0 0 1 0 2 0 1 1 0 1 1 1 1 0 0
147 | 2 0 2 0 0 1 1 1 0 0 0 0 1 2 2 0 1 1 0 0 0 2 0 1 1 0 1 0 1 1 1 0 1 1 1 1 0 0 1 2 0 0 1 1 1 0 2 0 1 0 2 1 1 1 1 0 1 0 0 1 1 1 0 1 0 0 0 2 0 0 1 0 1 0 0 1 0 0 1 0 0 0 0 1 1 1 1 0 0 0 0 2 1 0 0 0 2 0 0 1 0
148 | 2 1 1 1 0 1 1 0 1 1 2 0 0 1 0 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 1 1 2 0 2 0 0 2 0 0 0 1 2 0 0 1 0 2 2 0 1 0 2 0 1 0 0 0 0 1 0 1 0 1 0 1 0 0 0 0 0 0 0 0 1 0 1 0 0 0 1 1 1 1 0 1 0 0 1 0 1 1 1 1 0 2 0 1 2 0 0
149 | 0 1 1 1 0 1 0 1 0 1 1 0 1 0 0 0 0 1 0 0 1 2 0 0 0 1 1 1 0 0 0 2 1 1 1 0 1 0 0 1 0 0 0 1 0 1 0 0 1 1 0 1 0 1 0 0 1 0 0 2 1 1 0 2 1 0 1 1 0 0 0 1 1 1 1 0 1 1 2 1 0 0 2 1 0 1 0 0 1 1 0 0 0 0 1 0 1 0 1 1 0
150 | 1 0 0 0 0 2 1 1 1 0 2 0 0 0 0 0 1 0 0 0 1 1 0 0 0 2 1 0 0 1 1 0 0 1 1 0 0 1 1 1 0 0 0 0 1 1 0 0 1 1 0 0 0 1 1 0 0 0 0 0 1 0 1 1 0 0 1 0 0 0 2 0 0 0 0 1 2 1 0 1 0 0 0 0 0 0 0 1 0 2 0 2 0 0 1 1 2 1 0 0 0
151 | 2 0 0 1 1 2 2 0 1 1 0 0 1 2 1 0 1 0 2 0 0 0 1 0 0 1 1 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 2 0 1 1 1 1 0 0 1 1 0 1 0 0 0 1 0 2 2 1 0 0 0 0 0 1 0 0 1 1 1 1 2 1 0 1 1 0 0 0 0 1 2 1 1 1 0 0
152 | 0 1 0 0 0 1 0 1 0 1 0 1 2 1 1 0 1 2 1 0 1 1 1 2 2 0 0 0 1 2 0 2 1 0 0 1 0 0 1 1 1 0 1 2 0 0 1 0 1 0 0 1 0 0 0 1 1 1 1 0 1 0 0 1 0 0 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 1 2 0 1 1 0 2 0 1 0 0 0 1 0 1 0 1 1 0
153 | 1 0 0 0 1 1 0 2 2 1 0 0 1 0 0 1 1 1 0 1 0 0 0 0 1 2 1 0 2 0 1 1 1 0 2 1 1 1 1 0 0 1 0 0 1 0 0 0 2 2 0 1 0 0 1 1 1 0 0 1 0 1 0 1 0 0 1 0 0 1 0 2 1 0 1 1 1 0 0 1 0 0 0 0 1 0 1 0 0 0 1 0 0 1 2 0 0 2 0 1 0
154 | 1 2 0 1 0 1 0 0 1 1 2 0 1 2 1 0 0 0 0 1 1 0 0 0 1 0 0 2 0 1 1 1 1 2 1 1 2 1 0 1 0 1 0 1 1 0 0 0 1 2 0 1 0 0 1 1 0 1 0 1 0 0 1 1 0 1 1 0 0 1 0 0 0 1 0 1 2 0 0 0 1 1 0 0 0 0 1 1 2 0 1 0 0 2 1 1 1 0 0 0 0
155 | 0 0 0 1 1 1 0 0 0 1 2 2 1 1 1 1 2 0 2 2 2 1 0 0 0 0 0 0 1 1 0 2 0 1 1 1 0 0 0 0 1 2 1 1 2 0 0 0 0 1 1 1 1 0 1 0 0 0 1 1 0 0 0 0 0 0 0 2 0 0 0 1 0 1 0 1 1 1 1 0 2 2 2 1 1 1 1 0 1 0 0 0 2 0 0 1 1 1 1 0 0
156 | 0 0 2 0 1 0 1 0 0 1 1 0 1 0 0 0 0 1 0 1 0 0 1 0 0 1 0 1 2 1 1 1 2 0 1 1 0 0 0 1 0 0 0 1 1 1 2 0 1 0 2 0 0 1 1 2 1 0 0 1 0 1 0 1 0 2 0 0 1 1 2 0 1 0 0 1 0 1 1 0 0 2 1 0 0 1 1 0 0 1 2 0 0 1 0 0 1 0 0 0 0
157 | 1 1 0 1 1 0 0 1 1 0 0 1 2 1 1 2 2 1 1 1 0 1 0 0 1 1 0 0 1 1 0 1 1 0 0 0 1 1 1 0 1 1 2 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 1 1 0 1 1 1 1 0 0 0 1 1 0 0 1 0 1 0 1 0 0 1 1 0 1 0 0 0 1 0 0 1 0
158 | 0 1 0 2 0 0 0 1 1 1 1 0 0 1 0 0 1 1 1 0 0 0 1 0 0 0 1 0 0 0 0 1 1 1 0 1 1 1 1 0 1 1 0 0 0 1 0 1 2 0 0 2 1 0 2 1 1 1 0 2 0 1 0 1 0 0 0 1 0 0 2 2 1 1 2 0 1 0 1 1 1 0 1 0 0 0 0 0 0 0 2 0 1 1 1 0 0 2 1 1 0
159 | 2 0 1 2 0 0 1 0 1 1 1 0 0 0 1 1 0 0 0 1 0 1 0 0 2 1 0 0 2 1 0 1 2 0 0 1 0 0 0 0 1 1 1 2 0 1 1 1 1 2 0 0 1 0 1 0 1 0 1 0 0 1 0 2 1 0 0 1 0 1 1 0 1 2 1 1 0 0 0 1 0 0 1 0 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 0
160 | 0 0 1 1 2 0 1 1 1 0 1 0 0 1 0 0 0 1 1 0 0 0 1 0 2 0 1 0 1 0 0 0 0 1 0 0 0 1 1 1 1 0 0 0 1 0 1 0 2 0 0 1 1 1 1 0 1 1 1 1 0 0 0 2 0 0 0 0 0 0 1 0 0 0 0 1 0 0 0 0 0 2 2 1 2 1 0 2 0 1 1 1 2 0 1 1 0 0 0 0 0
161 | 1 0 0 0 2 1 0 0 1 1 0 1 0 0 0 1 1 0 0 1 1 0 0 1 0 2 0 2 1 0 0 1 2 1 1 0 0 0 0 0 0 1 0 1 1 1 1 0 1 1 0 2 0 0 0 0 0 1 1 2 0 0 0 0 0 2 0 2 1 2 0 0 1 2 1 0 1 0 0 0 0 0 0 2 0 1 0 1 2 2 1 0 1 0 1 0 1 1 1 0 0
162 | 1 1 0 1 2 1 1 0 1 0 1 1 0 1 1 0 2 1 2 0 0 1 0 1 0 1 2 0 0 1 1 1 1 2 0 0 1 1 0 1 0 1 0 0 2 0 0 1 0 0 1 1 0 0 0 1 0 2 0 1 1 1 0 0 0 2 1 1 0 2 1 0 2 2 1 0 0 0 1 0 0 0 0 1 1 0 0 1 1 0 1 1 2 0 1 0 0 1 1 0 0
163 | 0 0 0 2 1 1 1 0 2 0 0 1 2 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 1 0 0 0 2 0 1 1 1 0 1 1 1 0 1 1 1 0 1 0 1 0 2 0 0 0 0 2 2 0 1 1 2 1 1 1 1 2 1 1 2 0 0 0 1 0 1 0 2 1 1 0 0 1 1 0 0 1 0 2 0 0
164 | 0 1 0 2 1 0 1 2 1 1 1 1 0 0 1 0 0 0 0 0 1 1 0 1 0 0 0 1 1 2 2 1 0 2 1 2 1 1 1 1 1 0 0 2 1 0 0 1 0 1 2 1 1 1 1 0 1 1 0 1 1 0 1 0 1 1 1 2 1 0 2 1 0 1 1 0 1 0 0 2 2 1 1 0 1 1 1 1 2 0 1 1 0 1 1 0 0 2 0 0 0
165 | 0 1 0 0 0 0 1 0 0 1 0 1 0 0 1 0 0 2 1 1 2 1 0 0 1 0 0 2 0 0 2 0 1 0 0 1 0 0 1 0 0 1 1 0 1 1 1 0 1 1 0 1 0 2 2 1 0 1 0 1 1 1 1 0 0 1 1 1 1 0 0 0 0 0 0 1 1 1 1 1 0 2 0 0 1 0 0 1 0 0 0 0 0 0 1 2 1 0 2 0 0
166 | 0 1 1 1 0 0 0 1 1 2 1 1 0 0 0 0 1 0 0 0 0 0 1 1 0 1 1 1 1 0 1 1 0 1 2 2 1 1 1 0 1 2 0 0 0 0 0 0 0 0 0 1 0 0 1 0 0 1 1 1 2 0 1 0 0 0 0 1 1 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 2 1 1 0 0 0 0 0 1 1 0 2 2 1 0 0
167 | 1 1 0 0 2 0 0 1 1 1 0 0 0 0 1 2 1 1 0 0 1 1 0 0 2 0 0 1 1 2 1 0 0 1 1 0 1 0 0 0 1 2 1 0 0 0 1 1 1 2 1 0 1 0 0 0 1 1 1 2 2 0 1 2 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 2 1 1 0 0 0 0 0 1 0 1 1 1 0 2 0 0 2 2 0
168 | 0 1 1 1 0 1 1 1 1 0 0 2 1 0 0 1 0 1 1 1 1 1 1 0 0 1 0 0 1 0 0 1 0 0 0 0 0 0 0 0 1 0 0 0 2 0 1 1 1 1 1 1 1 0 0 0 1 0 0 0 0 1 0 1 0 0 1 1 2 1 0 1 0 0 0 0 0 0 1 1 1 2 1 0 0 0 0 2 2 0 0 0 2 1 0 1 1 0 1 0 0
169 | 1 0 0 1 1 0 0 1 2 0 0 0 2 1 0 0 1 1 0 1 0 0 0 1 2 0 0 1 0 0 1 0 0 1 0 1 1 0 0 2 1 1 0 1 0 1 1 1 2 0 1 0 2 1 0 0 1 1 0 2 1 1 0 1 1 1 0 0 1 0 2 0 0 1 1 0 0 0 1 0 2 1 0 1 0 2 0 0 1 1 0 1 1 1 0 2 0 0 0 0 0
170 | 1 0 0 1 0 0 0 1 0 0 0 0 0 2 0 0 0 1 0 1 1 1 2 0 1 1 1 0 2 2 1 1 0 0 1 0 0 2 1 0 0 2 0 1 0 2 1 1 0 0 1 0 0 1 0 1 0 0 0 0 1 0 1 1 1 1 1 1 1 1 0 1 1 1 0 1 2 0 2 1 0 0 0 0 1 1 0 1 1 0 1 0 1 2 1 1 0 1 1 0 0
171 | 0 1 0 1 0 0 1 1 0 0 0 1 1 0 2 0 1 1 1 1 0 1 1 0 1 0 0 2 1 0 1 1 1 0 2 2 1 1 2 1 0 1 0 1 1 2 1 0 1 0 2 1 1 1 2 0 1 0 0 1 0 1 1 2 1 1 0 1 0 0 0 1 1 0 1 0 1 1 0 1 1 0 1 2 0 1 1 1 1 0 1 1 2 0 0 1 1 0 0 2 0
172 | 1 2 1 1 1 0 0 0 0 1 1 1 0 0 1 0 0 0 0 1 2 1 1 0 0 1 1 1 0 0 0 1 1 1 1 1 0 1 0 2 0 1 0 0 1 0 1 0 0 2 1 0 1 0 0 0 2 1 1 1 0 2 0 2 2 2 0 0 0 1 0 0 0 1 1 1 0 0 0 1 0 0 1 1 2 1 1 0 1 1 1 1 2 1 1 0 2 1 1 2 0
173 | 2 1 1 0 0 0 1 0 1 1 1 1 1 1 1 0 0 0 1 1 1 1 0 1 0 1 2 1 0 1 1 0 1 1 1 1 0 2 1 2 0 0 0 0 1 1 1 1 1 1 2 1 1 1 1 1 1 0 0 2 1 0 0 0 1 1 1 1 0 1 0 1 1 0 1 1 1 0 0 0 1 0 0 0 0 1 0 0 0 0 0 1 2 0 2 0 1 2 1 2 0
174 | 1 1 0 0 0 0 1 0 1 0 1 1 1 2 0 1 2 0 0 0 2 0 0 1 1 1 0 2 0 0 1 2 0 1 0 1 0 0 0 2 0 1 1 0 1 1 1 0 1 0 1 0 0 2 1 1 0 0 0 1 0 2 2 0 1 1 0 0 0 0 2 0 0 1 0 0 0 0 1 1 1 0 1 1 1 1 0 1 2 1 0 1 2 1 0 1 1 1 0 1 0
175 | 0 1 0 1 0 0 0 1 1 0 1 0 1 1 0 1 1 1 1 1 0 1 1 1 0 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 0 1 1 2 0 0 0 0 1 1 0 0 0 2 1 1 1 1 0 0 0 1 1 0 0 1 1 1 1 1 0 1 0 1 0 2 2 1 0 1 0 1 0 0 2 1 0 0 0 0 2 0 0 2 1 0 1 0 0 0 0
176 | 0 0 0 1 0 0 1 1 0 0 1 0 0 0 1 1 0 0 0 0 0 0 0 2 0 1 1 0 0 0 0 1 0 1 0 0 1 0 1 2 0 1 0 0 0 1 0 1 0 1 1 1 0 1 0 0 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 1 0 1 1 1 1 0 1 0 0 0 1 1 2 1 1 0 1 0 1 1 0 0 1 0 0 0 0 0 0
177 | 1 1 0 2 1 0 0 0 0 1 1 1 1 1 0 1 1 2 0 0 0 0 0 0 0 0 0 1 0 1 1 0 1 1 0 1 0 1 0 1 0 0 1 1 0 1 1 1 0 0 1 1 1 0 2 0 1 0 0 2 0 1 1 0 0 1 1 2 0 0 0 1 0 1 0 1 0 0 2 0 1 0 1 0 0 1 1 2 0 0 1 1 1 0 0 0 0 0 0 1 0
178 | 1 0 1 1 1 1 0 1 1 0 0 0 2 0 2 1 0 0 0 0 0 1 0 0 0 0 1 0 0 1 0 1 0 0 0 0 0 0 1 0 0 2 0 0 1 1 0 1 0 0 0 0 0 1 1 1 0 1 1 1 1 1 1 2 1 0 2 0 1 0 0 0 1 0 0 1 0 1 1 1 0 1 1 0 0 1 1 0 0 1 1 1 2 0 1 2 1 1 0 0 0
179 | 2 2 0 0 0 1 0 1 0 1 2 0 0 0 1 0 0 0 1 0 0 2 1 0 1 1 1 0 2 0 0 1 0 0 0 1 0 1 0 1 2 1 1 1 0 2 0 0 0 0 1 1 0 0 1 0 1 1 0 0 1 0 0 2 0 0 1 0 1 1 1 1 0 1 2 0 1 2 1 1 1 0 1 0 0 0 1 1 1 0 0 2 0 1 0 1 0 1 0 1 0
180 | 1 1 0 0 0 0 1 1 1 0 0 0 0 1 0 1 0 1 1 2 1 2 1 2 0 1 0 0 1 1 0 0 0 1 1 0 0 0 1 0 0 0 0 1 0 1 1 0 0 1 0 0 0 1 1 0 1 1 0 0 1 0 1 1 0 0 0 0 1 0 1 0 2 0 0 0 1 1 1 0 2 1 1 0 0 0 0 0 0 0 1 0 0 1 2 1 1 2 1 0 0
181 | 0 1 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 0 0 1 1 1 0 1 1 2 0 1 0 0 0 0 0 1 0 0 1 2 1 1 1 0 0 1 0 1 1 0 2 1 1 0 0 0 1 2 1 2 1 1 1 0 0 0 1 1 0 2 0 1 0 0 0 0 1 1 0 1 2 0 1 0 1 0 0 1 2 1 1 1 0 0 1 0 0 1 1 1 1 1 0
182 | 0 1 0 0 1 1 0 1 2 0 0 2 0 0 0 0 1 2 0 0 0 0 0 1 0 1 1 1 1 1 0 0 1 0 1 1 0 1 0 1 0 1 0 0 2 1 1 0 2 1 0 1 1 2 1 1 0 1 0 0 1 1 0 0 1 0 0 2 1 2 0 2 0 2 0 1 1 0 0 0 1 2 0 0 1 1 1 0 0 0 0 1 0 2 0 1 0 1 1 0 0
183 | 1 1 1 1 0 1 1 0 0 1 0 0 0 0 1 1 0 1 0 0 0 0 1 2 0 2 0 0 1 0 0 1 0 0 0 1 0 1 2 0 0 0 0 1 0 1 1 0 0 0 0 1 0 1 0 2 1 0 0 1 0 0 0 1 0 2 0 1 1 1 1 0 0 1 0 1 2 1 1 0 1 0 1 0 0 0 0 1 0 2 2 1 0 1 0 0 1 1 2 0 0
184 | 0 1 0 0 1 1 1 1 1 1 0 1 1 0 1 1 2 0 1 1 0 1 0 1 2 1 1 0 0 0 1 0 2 1 0 1 0 1 1 2 0 1 1 1 1 0 0 0 0 2 0 0 1 0 0 1 0 0 1 0 0 0 0 0 1 0 0 1 0 0 1 1 0 1 1 1 1 2 0 1 1 1 0 0 1 1 0 2 1 0 0 0 0 1 2 1 1 1 0 1 0
185 | 1 0 0 2 1 2 2 0 0 0 1 0 0 0 0 2 0 0 0 1 1 0 0 0 1 1 0 0 0 1 1 0 1 0 2 1 0 0 0 1 0 0 0 0 1 0 1 1 1 1 0 0 0 1 2 1 1 1 2 0 1 0 1 1 2 1 0 1 1 1 0 0 1 0 1 1 1 1 0 0 1 0 2 1 0 2 2 2 0 0 0 1 1 1 1 0 0 1 0 0 0
186 | 0 2 0 0 0 0 2 0 1 1 0 0 0 0 0 0 0 0 0 2 0 1 0 0 0 1 2 0 0 0 1 0 0 0 1 1 1 1 2 0 0 0 0 1 0 0 0 1 0 1 1 2 0 0 1 1 0 0 1 1 1 0 0 1 0 1 0 0 1 1 0 1 0 1 1 0 0 0 0 0 0 0 1 2 2 1 1 1 0 1 0 0 1 2 0 1 0 0 2 0 0
187 | 0 0 1 1 1 2 0 2 1 0 1 0 1 0 1 1 1 1 1 0 2 0 1 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 1 1 2 0 0 0 1 1 0 1 1 0 1 1 0 0 1 0 1 1 1 1 0 1 0 0 0 1 0 1 0 0 0 1 1 1 0 2 1 0 0 0 0 0 1 0 1 1 0 1 0 1 1 2 0 0 1 0 1 0
188 | 0 0 0 0 0 0 2 2 0 0 0 1 1 2 1 0 0 1 2 1 0 2 1 0 0 1 2 0 0 0 0 0 1 1 0 1 0 0 0 0 1 1 0 0 1 0 1 0 0 1 1 0 0 0 1 1 0 0 2 0 1 1 0 0 1 1 2 1 0 1 1 1 1 0 0 1 1 0 0 1 1 0 0 1 1 0 1 0 2 0 1 0 1 0 0 1 0 1 0 1 0
189 | 1 2 0 0 1 1 2 1 0 2 1 1 0 1 2 2 0 0 0 0 1 0 0 1 1 0 1 1 2 1 1 1 1 1 1 0 0 0 0 0 0 2 1 1 2 1 0 0 0 0 0 0 1 0 1 0 1 1 0 1 1 0 0 1 0 1 1 0 1 0 0 2 0 1 1 2 0 1 0 0 0 0 0 1 1 0 1 1 0 0 0 2 0 0 0 1 0 1 1 1 0
190 | 0 1 1 0 1 1 0 0 0 0 0 0 2 0 1 0 1 1 0 0 1 1 0 0 0 1 0 2 1 0 2 2 1 0 1 1 0 0 0 1 0 1 0 0 1 1 0 0 1 2 0 1 1 1 1 0 1 1 1 1 1 2 1 1 1 1 0 0 1 1 0 1 0 2 0 1 1 0 0 1 1 1 1 0 0 1 2 1 0 1 1 1 0 1 1 0 2 2 0 1 0
191 | 2 2 1 1 1 0 0 0 2 1 0 1 0 1 0 0 1 0 1 1 1 0 1 1 0 1 2 1 0 1 0 1 2 1 1 1 1 0 0 0 1 0 0 0 0 1 1 0 1 1 0 0 0 1 0 1 1 2 0 0 1 0 1 1 0 0 2 0 0 0 1 0 2 0 0 2 0 1 1 0 0 1 1 0 1 0 0 0 0 2 0 2 2 0 1 1 2 1 2 1 0
192 | 0 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 0 1 0 2 1 0 1 1 0 0 0 1 0 0 1 0 0 0 1 2 1 1 0 0 1 2 1 2 1 1 0 1 1 0 0 0 0 2 1 2 0 0 1 1 0 0 2 2 0 0 1 0 0 1 1 0 0 1 0 0 1 0 0 1 1 1 1 2 0 1 0 2 0 1 0 0 0 0 0 1 1 2 0 0
193 | 0 0 0 0 1 1 1 0 1 1 0 0 0 0 1 0 0 1 0 0 0 1 1 1 1 1 1 1 0 1 0 0 0 1 1 1 0 1 1 1 1 0 0 1 0 1 0 1 1 0 0 1 0 2 0 1 1 0 0 1 0 0 0 1 0 2 0 1 2 1 0 0 1 1 0 1 0 1 0 0 0 1 0 0 1 1 1 2 0 0 1 1 0 2 0 0 1 1 0 1 0
194 | 1 0 0 1 0 0 2 1 1 1 1 2 1 0 1 0 0 0 0 1 0 2 0 0 2 0 0 1 2 0 1 1 1 1 0 0 0 0 0 1 0 0 1 2 1 1 1 0 1 0 0 0 0 1 2 2 0 2 0 1 0 0 0 0 0 0 0 1 1 1 1 0 1 1 1 1 0 0 0 0 2 0 0 0 1 0 0 1 1 1 0 1 1 2 2 0 0 0 0 1 0
195 | 0 1 1 0 0 0 0 1 1 1 0 0 2 1 0 0 2 1 0 2 1 2 2 0 1 1 1 0 1 0 0 0 0 1 0 0 0 1 1 1 0 1 2 0 1 1 1 0 0 0 0 0 1 0 0 2 0 1 1 1 2 0 2 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 1 2 1 0 0 1 1 0 0 0 0 0 1 0 2 0 1 0 0
196 | 1 1 1 1 0 0 0 1 0 1 0 0 1 0 0 0 1 2 0 1 0 1 1 0 0 0 0 0 1 1 1 1 0 1 0 0 0 0 0 0 0 0 0 2 1 2 2 0 0 1 0 1 2 1 0 1 1 1 0 0 1 1 0 0 1 1 2 0 1 2 0 0 1 1 0 0 1 0 0 1 1 0 0 0 1 1 0 0 1 0 0 1 0 1 0 1 2 0 1 1 0
197 | 0 0 0 1 2 0 0 0 1 0 1 1 1 1 2 2 2 0 1 0 0 0 0 0 2 0 1 0 0 1 0 1 0 0 1 1 0 2 1 0 0 0 0 0 0 0 0 1 1 1 1 0 1 1 0 0 1 0 0 0 2 1 2 1 1 2 1 1 0 0 1 0 0 1 2 0 1 1 0 1 0 2 0 1 1 0 1 2 0 1 1 0 1 0 1 0 0 1 0 0 0
198 | 1 0 1 1 0 0 0 1 0 0 2 0 0 0 1 1 1 0 0 1 0 0 1 0 1 1 2 0 0 1 1 0 0 1 1 1 2 0 2 0 0 1 0 1 1 1 2 0 0 1 1 0 0 0 1 0 0 0 2 1 1 0 1 0 0 0 1 0 0 0 0 1 1 1 0 0 1 0 0 1 1 1 0 0 0 0 0 1 1 0 1 0 1 1 1 0 1 0 1 0 0
199 | 1 1 2 1 0 1 1 1 1 1 1 1 0 2 0 0 1 0 0 0 1 0 0 1 0 2 1 0 0 0 1 0 1 1 0 0 0 2 0 0 0 0 1 0 1 1 2 0 0 0 0 1 1 0 2 0 1 1 1 0 1 1 1 1 0 1 0 1 0 1 1 0 1 1 1 0 2 0 1 0 0 0 1 1 1 1 1 0 0 0 1 1 2 0 0 1 1 0 0 0 0
200 | 1 1 2 1 0 0 0 0 0 0 1 1 1 0 0 0 0 1 0 1 0 1 1 0 0 0 1 1 1 0 1 2 1 0 1 1 1 1 0 0 1 2 0 0 0 0 1 0 2 1 0 0 0 1 0 1 0 0 0 2 0 0 1 0 2 2 1 1 1 2 1 1 0 1 1 1 0 1 0 0 0 1 0 0 0 1 1 1 1 1 0 1 1 0 1 0 1 1 0 1 0
201 | 0 2 1 0 0 0 1 0 1 0 1 0 1 0 0 1 1 0 1 0 0 1 2 2 1 0 2 0 0 0 1 1 0 1 0 1 0 0 0 0 0 1 1 2 0 1 0 1 1 1 1 1 1 1 0 1 1 0 2 1 0 0 1 0 1 1 1 0 0 1 1 0 0 1 0 1 0 0 1 0 0 1 1 1 1 1 0 1 0 1 0 0 2 0 0 0 1 0 1 0 0
202 |
--------------------------------------------------------------------------------
/papers/Lexicase Selection.pdf:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/EvoML/evolved-forests/7572d5bcf38cfed643bcd42f9f3461aa7b102f17/papers/Lexicase Selection.pdf
--------------------------------------------------------------------------------
/subsampling/.ipynb_checkpoints/Difficulty Level of Rows - Approach - Results-checkpoint.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "### Aim \n",
8 | "What I tried was with the intuition that if we provide a difficulty level to each of the row and allot the fitness function according to this difficulty then we can make ensemble of models which tries to solve different chunks of the dataset.\n",
9 | "\n",
10 | "-------------------------------\n",
11 | "\n",
12 | "#### Difficulty Marks\n",
13 | " - The difficulty would define that how many models were able to predict the exact value for that row. If the model is difficult then more marks will be awarded to the model on solving it.\n",
14 | " \n",
15 | " - We tried\n",
16 | " \n",
17 | " -- Discrete level of marking (ex: 0-10)\n",
18 | " \n",
19 | " -- -ve of Marks as per how many models have solved it. So higher magnitude on the -ve side would indicate many models have already solved it and now this model should focus on other rows.\n",
20 | " \n",
21 | " -- 1/(number of pop that has solved it)\n",
22 | " \n",
23 | " -- Again incremental marks but with upper and lower margin.\n",
24 | "\n",
25 | "-------------------------------------\n",
26 | "\n",
27 | "#### When to update the marks\n",
28 | " \n",
29 | " - We updated the score after each fitness calculation for each model, so that the models below in the population can focus on the harder problems. \n",
30 | " \n",
31 | " - Updated the score after each population iteration, so that in the next iteration the models can focus on harder rows of the dataset.\n",
32 | " \n",
33 | "-------------------------------------\n",
34 | "\n",
35 | "#### Results\n",
36 | " \n",
37 | " - But the results were not inline with our expectations. the difficulty seemed to be changing from one end to the other. It keeps on fluctuating during the iterations.\n",
38 | " \n",
39 | " - Also the accuracy kept on fluctuating. If we run it for multiple iterations, it wins sometimes but loses to the basic models most of the time.\n"
40 | ]
41 | }
42 | ],
43 | "metadata": {
44 | "kernelspec": {
45 | "display_name": "Python 2",
46 | "language": "python",
47 | "name": "python2"
48 | },
49 | "language_info": {
50 | "codemirror_mode": {
51 | "name": "ipython",
52 | "version": 2
53 | },
54 | "file_extension": ".py",
55 | "mimetype": "text/x-python",
56 | "name": "python",
57 | "nbconvert_exporter": "python",
58 | "pygments_lexer": "ipython2",
59 | "version": "2.7.9"
60 | }
61 | },
62 | "nbformat": 4,
63 | "nbformat_minor": 0
64 | }
65 |
--------------------------------------------------------------------------------
/subsampling/Difficulty Level of Rows - Approach - Results.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "### Aim \n",
8 | "What I tried was with the intuition that if we provide a difficulty level to each of the row and allot the fitness function according to this difficulty then we can make ensemble of models which tries to solve different chunks of the dataset.\n",
9 | "\n",
10 | "-------------------------------\n",
11 | "\n",
12 | "#### Difficulty Marks\n",
13 | " - The difficulty would define that how many models were able to predict the exact value for that row. If the model is difficult then more marks will be awarded to the model on solving it.\n",
14 | " \n",
15 | " - We tried\n",
16 | " \n",
17 | " -- Discrete level of marking (ex: 0-10)\n",
18 | " \n",
19 | " -- -ve of Marks as per how many models have solved it. So higher magnitude on the -ve side would indicate many models have already solved it and now this model should focus on other rows.\n",
20 | " \n",
21 | " -- 1/(number of pop that has solved it)\n",
22 | " \n",
23 | " -- Again incremental marks but with upper and lower margin.\n",
24 | "\n",
25 | "-------------------------------------\n",
26 | "\n",
27 | "#### When to update the marks\n",
28 | " \n",
29 | " - We updated the score after each fitness calculation for each model, so that the models below in the population can focus on the harder problems. \n",
30 | " \n",
31 | " - Updated the score after each population iteration, so that in the next iteration the models can focus on harder rows of the dataset.\n",
32 | " \n",
33 | "-------------------------------------\n",
34 | "\n",
35 | "#### Results\n",
36 | " \n",
37 | " - But the results were not inline with our expectations. the difficulty seemed to be changing from one end to the other. It keeps on fluctuating during the iterations.\n",
38 | " \n",
39 | " - Also the accuracy kept on fluctuating. If we run it for multiple iterations, it wins sometimes but loses to the basic models most of the time.\n"
40 | ]
41 | }
42 | ],
43 | "metadata": {
44 | "kernelspec": {
45 | "display_name": "Python 2",
46 | "language": "python",
47 | "name": "python2"
48 | },
49 | "language_info": {
50 | "codemirror_mode": {
51 | "name": "ipython",
52 | "version": 2
53 | },
54 | "file_extension": ".py",
55 | "mimetype": "text/x-python",
56 | "name": "python",
57 | "nbconvert_exporter": "python",
58 | "pygments_lexer": "ipython2",
59 | "version": "2.7.9"
60 | }
61 | },
62 | "nbformat": 4,
63 | "nbformat_minor": 0
64 | }
65 |
--------------------------------------------------------------------------------
/subsampling/dump/evolved-forest-demo.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "code",
5 | "execution_count": 484,
6 | "metadata": {
7 | "collapsed": true
8 | },
9 | "outputs": [],
10 | "source": [
11 | "tools.selTournament?="
12 | ]
13 | },
14 | {
15 | "cell_type": "code",
16 | "execution_count": 488,
17 | "metadata": {
18 | "collapsed": false,
19 | "scrolled": false
20 | },
21 | "outputs": [
22 | {
23 | "name": "stdout",
24 | "output_type": "stream",
25 | "text": [
26 | "Base DecisionTreeClassifier accuracy: 0.79822616408\n",
27 | "Base RandomForestClassifier accuracy: 0.935698447894\n",
28 | "Base GradientBoostingClassifier accuracy: 0.953436807095\n",
29 | "\n",
30 | "gen\tnevals\tstd \tmin \tavg \tmax \n",
31 | "0 \t100 \t0.29591\t1e-20\t0.283743\t0.845697\n",
32 | "1 \t74 \t0.286518\t1e-20\t0.265852\t0.845697\n",
33 | "0.377744807122\n",
34 | "0.153958785316\n",
35 | "0.838137472284\n",
36 | "2 \t70 \t0.298589\t1e-20\t0.298604\t0.863501\n",
37 | "0.418545994065\n",
38 | "0.178662031471\n",
39 | "0.835920177384\n",
40 | "3 \t71 \t0.296482\t1e-20\t0.312243\t0.851632\n",
41 | "0.448872403561\n",
42 | "0.175614031596\n",
43 | "0.835920177384\n",
44 | "4 \t71 \t0.297173\t1e-20\t0.314295\t0.866469\n",
45 | "0.456112759644\n",
46 | "0.172477157744\n",
47 | "0.835920177384\n",
48 | "5 \t71 \t0.287548\t1e-20\t0.300233\t0.851632\n",
49 | "0.432551928783\n",
50 | "0.16791387545\n",
51 | "0.853658536585\n",
52 | "6 \t79 \t0.291583\t1e-20\t0.33621 \t0.851632\n",
53 | "0.475044510386\n",
54 | "0.197375676971\n",
55 | "0.851441241685\n",
56 | "7 \t83 \t0.288972\t1e-20\t0.325717\t0.860534\n",
57 | "0.46590504451\n",
58 | "0.185528906462\n",
59 | "0.862527716186\n",
60 | "8 \t73 \t0.28661 \t1e-20\t0.302143\t0.860534\n",
61 | "0.429317507418\n",
62 | "0.174967656034\n",
63 | "0.875831485588\n",
64 | "9 \t71 \t0.281472\t1e-20\t0.300827\t0.872404\n",
65 | "0.423560830861\n",
66 | "0.178092622587\n",
67 | "0.871396895787\n",
68 | "10 \t70 \t0.288094\t1e-20\t0.292819\t0.872404\n",
69 | "0.410652818991\n",
70 | "0.174985296847\n",
71 | "0.875831485588\n",
72 | "11 \t76 \t0.293143\t1e-20\t0.295748\t0.872404\n",
73 | "0.415400593472\n",
74 | "0.176095295506\n",
75 | "0.866962305987\n",
76 | "12 \t71 \t0.289762\t1e-20\t0.273901\t0.872404\n",
77 | "0.392433234421\n",
78 | "0.155369342742\n",
79 | "0.860310421286\n",
80 | "13 \t72 \t0.295138\t1e-20\t0.291235\t0.857567\n",
81 | "0.416320474777\n",
82 | "0.166150042546\n",
83 | "0.844789356984\n",
84 | "14 \t73 \t0.294758\t1e-20\t0.292039\t0.863501\n",
85 | "0.42765578635\n",
86 | "0.156421940329\n",
87 | "0.840354767184\n",
88 | "15 \t69 \t0.296153\t1e-20\t0.299076\t0.848665\n",
89 | "0.430652818991\n",
90 | "0.167498226142\n",
91 | "0.835920177384\n",
92 | "16 \t79 \t0.296001\t1e-20\t0.328756\t0.845697\n",
93 | "0.477952522255\n",
94 | "0.179559713439\n",
95 | "0.840354767184\n",
96 | "17 \t71 \t0.29285 \t1e-20\t0.317651\t0.845697\n",
97 | "0.461364985163\n",
98 | "0.173936089295\n",
99 | "0.849223946785\n",
100 | "18 \t68 \t0.29147 \t1e-20\t0.316392\t0.848665\n",
101 | "0.462759643917\n",
102 | "0.17002515921\n",
103 | "0.853658536585\n",
104 | "19 \t67 \t0.298827\t1e-20\t0.329119\t0.866469\n",
105 | "0.476824925816\n",
106 | "0.181412345922\n",
107 | "0.840354767184\n",
108 | "20 \t67 \t0.298097\t1e-20\t0.327061\t0.866469\n",
109 | "0.472611275964\n",
110 | "0.181510332729\n",
111 | "0.853658536585\n",
112 | "21 \t76 \t0.28923 \t1e-20\t0.307447\t0.866469\n",
113 | "0.442314540059\n",
114 | "0.172578744847\n",
115 | "0.855875831486\n",
116 | "22 \t73 \t0.285179\t1e-20\t0.32282 \t0.854599\n",
117 | "0.457210682493\n",
118 | "0.188430112702\n",
119 | "0.889135254989\n",
120 | "23 \t78 \t0.284747\t1e-20\t0.32154 \t0.845697\n",
121 | "0.452136498516\n",
122 | "0.19094445343\n",
123 | "0.875831485588\n",
124 | "24 \t73 \t0.28068 \t1e-20\t0.322317\t0.860534\n",
125 | "0.448931750742\n",
126 | "0.19570142394\n",
127 | "0.866962305987\n",
128 | "25 \t81 \t0.27866 \t1e-20\t0.317622\t0.860534\n",
129 | "0.445519287834\n",
130 | "0.18972445102\n",
131 | "0.889135254989\n",
132 | "26 \t77 \t0.284933\t1e-20\t0.328825\t0.860534\n",
133 | "0.466884272997\n",
134 | "0.190766626822\n",
135 | "0.889135254989\n",
136 | "27 \t70 \t0.280739\t1e-20\t0.324522\t0.848665\n",
137 | "0.45884272997\n",
138 | "0.190202114038\n",
139 | "0.89800443459\n",
140 | "28 \t81 \t0.282713\t1e-20\t0.337602\t0.860534\n",
141 | "0.477151335312\n",
142 | "0.198052890902\n",
143 | "0.882483370288\n",
144 | "29 \t77 \t0.29214 \t1e-20\t0.350574\t0.860534\n",
145 | "0.502225519288\n",
146 | "0.198922673136\n",
147 | "0.880266075388\n",
148 | "30 \t80 \t0.290774\t1e-20\t0.327165\t0.851632\n",
149 | "0.471424332344\n",
150 | "0.18290616772\n",
151 | "0.882483370288\n",
152 | "31 \t68 \t0.292776\t1e-20\t0.34284 \t0.851632\n",
153 | "0.496053412463\n",
154 | "0.189626659651\n",
155 | "0.844789356984\n",
156 | "32 \t72 \t0.291386\t1e-20\t0.353735\t0.857567\n",
157 | "0.511721068249\n",
158 | "0.195749254913\n",
159 | "0.847006651885\n",
160 | "33 \t71 \t0.289644\t1e-20\t0.326167\t0.851632\n",
161 | "0.468516320475\n",
162 | "0.183816854062\n",
163 | "0.860310421286\n",
164 | "34 \t76 \t0.280292\t1e-20\t0.311883\t0.860534\n",
165 | "0.442077151335\n",
166 | "0.181687974098\n",
167 | "0.882483370288\n",
168 | "35 \t63 \t0.276928\t1e-20\t0.32416 \t0.869436\n",
169 | "0.451928783383\n",
170 | "0.196391914623\n",
171 | "0.886917960089\n",
172 | "36 \t81 \t0.27892 \t1e-20\t0.315959\t0.869436\n",
173 | "0.435400593472\n",
174 | "0.196517872464\n",
175 | "0.875831485588\n",
176 | "37 \t76 \t0.28591 \t1e-20\t0.313277\t0.869436\n",
177 | "0.437329376855\n",
178 | "0.189225307873\n",
179 | "0.871396895787\n",
180 | "38 \t79 \t0.285711\t1e-20\t0.31892 \t0.860534\n",
181 | "0.446735905045\n",
182 | "0.191104504858\n",
183 | "0.884700665188\n",
184 | "39 \t70 \t0.292208\t1e-20\t0.326781\t0.854599\n",
185 | "0.4646884273\n",
186 | "0.188872948757\n",
187 | "0.875831485588\n",
188 | "40 \t72 \t0.284664\t1e-20\t0.33649 \t0.863501\n",
189 | "0.479792284866\n",
190 | "0.193188312152\n",
191 | "0.871396895787\n",
192 | "41 \t76 \t0.286813\t1e-20\t0.333206\t0.863501\n",
193 | "0.472670623145\n",
194 | "0.193742314499\n",
195 | "0.871396895787\n",
196 | "42 \t80 \t0.289526\t1e-20\t0.325685\t0.863501\n",
197 | "0.465133531157\n",
198 | "0.186237456756\n",
199 | "0.878048780488\n",
200 | "43 \t79 \t0.291194\t1e-20\t0.321169\t0.863501\n",
201 | "0.460860534125\n",
202 | "0.181476933701\n",
203 | "0.866962305987\n",
204 | "44 \t82 \t0.29731 \t1e-20\t0.315888\t0.863501\n",
205 | "0.457537091988\n",
206 | "0.174238962257\n",
207 | "0.860310421286\n",
208 | "45 \t78 \t0.288061\t1e-20\t0.312245\t0.863501\n",
209 | "0.454629080119\n",
210 | "0.169860825014\n",
211 | "0.858093126386\n",
212 | "46 \t69 \t0.291955\t1e-20\t0.317767\t0.863501\n",
213 | "0.45234421365\n",
214 | "0.183188930581\n",
215 | "0.860310421286\n",
216 | "47 \t70 \t0.296527\t1e-20\t0.299055\t0.863501\n",
217 | "0.42762611276\n",
218 | "0.170484506309\n",
219 | "0.855875831486\n",
220 | "48 \t73 \t0.292385\t1e-20\t0.273031\t0.860534\n",
221 | "0.396112759644\n",
222 | "0.14994997282\n",
223 | "0.847006651885\n",
224 | "49 \t78 \t0.287511\t1e-20\t0.29503 \t0.857567\n",
225 | "0.421246290801\n",
226 | "0.168813387474\n",
227 | "0.891352549889\n",
228 | "50 \t70 \t0.282213\t1e-20\t0.287907\t0.857567\n",
229 | "0.407299703264\n",
230 | "0.168514699756\n",
231 | "0.866962305987\n"
232 | ]
233 | },
234 | {
235 | "data": {
236 | "text/plain": [
237 | "'not_(or_(False, lt(if_then_else(False, add(Feature20, 3.0), if_then_else(False, Feature48, 7.0)), mul(if_then_else(False, Feature14, Feature8), if_then_else(False, Feature54, Feature36)))))'"
238 | ]
239 | },
240 | "execution_count": 488,
241 | "metadata": {},
242 | "output_type": "execute_result"
243 | }
244 | ],
245 | "source": [
246 | "import operator\n",
247 | "import itertools\n",
248 | "import numpy as np\n",
249 | "\n",
250 | "from deap import algorithms\n",
251 | "from deap import base\n",
252 | "from deap import creator\n",
253 | "from deap import tools\n",
254 | "from deap import gp\n",
255 | "\n",
256 | "from sklearn.tree import DecisionTreeClassifier\n",
257 | "from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifier\n",
258 | "from sklearn.datasets import load_digits\n",
259 | "from sklearn.cross_validation import train_test_split\n",
260 | "\n",
261 | "np.seterr(all='raise')\n",
262 | "\n",
263 | "digits = load_digits()\n",
264 | "digit_features, digit_labels = digits.data, digits.target\n",
265 | "\n",
266 | "\n",
267 | "X_train_tot, X_test_tot, y_train_tot, y_test_tot = train_test_split(digit_features, digit_labels, stratify=digit_labels,\n",
268 | " train_size=0.75, test_size=0.25)\n",
269 | "\n",
270 | "\n",
271 | "X_train, X_test, y_train, y_test = train_test_split(X_train_tot, y_train_tot, stratify=y_train_tot,\n",
272 | " train_size=0.75, test_size=0.25)\n",
273 | "\n",
274 | "\n",
275 | "\n",
276 | "\n",
277 | "# defined a new primitive set for strongly typed GP\n",
278 | "pset = gp.PrimitiveSetTyped('MAIN', itertools.repeat(float, digit_features.shape[1]), bool, 'Feature')\n",
279 | "\n",
280 | "# boolean operators\n",
281 | "pset.addPrimitive(operator.and_, [bool, bool], bool)\n",
282 | "pset.addPrimitive(operator.or_, [bool, bool], bool)\n",
283 | "pset.addPrimitive(operator.not_, [bool], bool)\n",
284 | "\n",
285 | "# floating point operators\n",
286 | "# Define a protected division function\n",
287 | "def protectedDiv(left, right):\n",
288 | " try: return left / right\n",
289 | " except (ZeroDivisionError, FloatingPointError): return 1.\n",
290 | "\n",
291 | "pset.addPrimitive(operator.add, [float, float], float)\n",
292 | "pset.addPrimitive(operator.sub, [float, float], float)\n",
293 | "pset.addPrimitive(operator.mul, [float, float], float)\n",
294 | "pset.addPrimitive(protectedDiv, [float, float], float)\n",
295 | "\n",
296 | "# logic operators\n",
297 | "# Define a new if-then-else function\n",
298 | "def if_then_else(in1, output1, output2):\n",
299 | " if in1: return output1\n",
300 | " else: return output2\n",
301 | "\n",
302 | "pset.addPrimitive(operator.lt, [float, float], bool)\n",
303 | "pset.addPrimitive(operator.eq, [float, float], bool)\n",
304 | "pset.addPrimitive(if_then_else, [bool, float, float], float)\n",
305 | "\n",
306 | "# terminals\n",
307 | "pset.addTerminal(False, bool)\n",
308 | "pset.addTerminal(True, bool)\n",
309 | "for val in np.arange(-10., 11.):\n",
310 | " pset.addTerminal(val, float)\n",
311 | "\n",
312 | "creator.create('FitnessMax', base.Fitness, weights=(2.0,1.0))\n",
313 | "creator.create('Individual', gp.PrimitiveTree, fitness=creator.FitnessMax)\n",
314 | "\n",
315 | "toolbox = base.Toolbox()\n",
316 | "toolbox.register('expr', gp.genHalfAndHalf, pset=pset, min_=1, max_=3)\n",
317 | "toolbox.register('individual', tools.initIterate, creator.Individual, toolbox.expr)\n",
318 | "toolbox.register('population', tools.initRepeat, list, toolbox.individual)\n",
319 | "toolbox.register('compile', gp.compile, pset=pset)\n",
320 | "\n",
321 | "\n",
322 | "\n",
323 | "row_prob = {} # Each key is row index,\n",
324 | "row_mean_prob = {}\n",
325 | "def update_true_class_variance(pop_):\n",
326 | " \n",
327 | " \"\"\"\n",
328 | " pop is a list of indiv.\n",
329 | " Each indiv\n",
330 | " \"\"\"\n",
331 | " global row_prob, row_mean_prob\n",
332 | " row_prob = {} #reset\n",
333 | " row_mean_prob = {}\n",
334 | " \n",
335 | " for individual in pop_:\n",
336 | " func = toolbox.compile(expr=individual)\n",
337 | " subsample = np.array([func(*record) for record in X_train])\n",
338 | "\n",
339 | " if X_train[subsample].shape[0] == 0:\n",
340 | " continue\n",
341 | "\n",
342 | " clf = DecisionTreeClassifier(random_state=34092)\n",
343 | " clf.fit(X_train[subsample], y_train[subsample])\n",
344 | "\n",
345 | " probas = clf.predict_proba(X_test)\n",
346 | "# print clf.classes_\n",
347 | " for ix, row in enumerate(probas):\n",
348 | "# print row\n",
349 | " try:\n",
350 | " true_p = row[y_test[ix]]\n",
351 | " except:\n",
352 | " true_p = np.nan\n",
353 | " try:\n",
354 | " row_prob[ix].append(true_p)\n",
355 | " except:\n",
356 | " row_prob[ix] = [true_p]\n",
357 | "# print row_prob[ix]\n",
358 | " \n",
359 | "# print 'row_prob', row_prob\n",
360 | " for key in row_prob.keys():\n",
361 | " row_mean_prob[key] = np.nanmean(row_prob[key])\n",
362 | " \n",
363 | " return \n",
364 | " \n",
365 | "\n",
366 | "def evaluate_individual(individual):\n",
367 | " global row_mean_prob\n",
368 | " # Transform the tree expression into a callable function\n",
369 | " func = toolbox.compile(expr=individual)\n",
370 | " subsample = np.array([func(*record) for record in X_train])\n",
371 | " \n",
372 | " if X_train[subsample].shape[0] == 0:\n",
373 | " return (1e-20,1e-20)\n",
374 | " \n",
375 | " clf = DecisionTreeClassifier(random_state=34092)\n",
376 | " clf.fit(X_train[subsample], y_train[subsample])\n",
377 | " score = clf.score(X_test, y_test)\n",
378 | " \n",
379 | " probas = clf.predict_proba(X_test)\n",
380 | " total_variance = []\n",
381 | " for ix, row in enumerate(probas):\n",
382 | " try:\n",
383 | " true_p = row[y_test[ix]]\n",
384 | " except:\n",
385 | " true_p = 0\n",
386 | " mean_p = row_mean_prob[ix]\n",
387 | " \n",
388 | " # Can also simply do - 1 - true_p and measure variance in the model.\n",
389 | " if true_p>=0.1:\n",
390 | " added_variance = (mean_p - true_p)**2\n",
391 | " else:\n",
392 | " added_variance = 0 #Should this be nan?\n",
393 | " total_variance.append(added_variance)\n",
394 | " \n",
395 | " mean_variance_added = np.sqrt(np.nanmean(total_variance))\n",
396 | " \n",
397 | " return (score, mean_variance_added)\n",
398 | " \n",
399 | "toolbox.register('evaluate', evaluate_individual)\n",
400 | "#todo: change this according to multi-objective\n",
401 | "# toolbox.register('select', tools.selTournament, tournsize=3)\n",
402 | "toolbox.register('select', tools.selNSGA2)\n",
403 | "\n",
404 | "toolbox.register('mate', gp.cxOnePoint)\n",
405 | "toolbox.register('expr_mut', gp.genFull, min_=0, max_=3)\n",
406 | "toolbox.register('mutate', gp.mutUniform, expr=toolbox.expr_mut, pset=pset)\n",
407 | "\n",
408 | "population = toolbox.population(n=100)\n",
409 | "halloffame = tools.HallOfFame(1)\n",
410 | "stats = tools.Statistics(lambda ind: ind.fitness.values)\n",
411 | "stats.register('std', np.std)\n",
412 | "stats.register('min', np.min)\n",
413 | "stats.register('avg', np.mean)\n",
414 | "stats.register('max', np.max)\n",
415 | "\n",
416 | "clf = DecisionTreeClassifier(random_state=34092)\n",
417 | "clf.fit(X_train_tot, y_train_tot)\n",
418 | "print('Base DecisionTreeClassifier accuracy: {}'.format(clf.score(X_test_tot, y_test_tot)))\n",
419 | "\n",
420 | "clf = RandomForestClassifier(random_state=34092)\n",
421 | "clf.fit(X_train_tot, y_train_tot)\n",
422 | "print('Base RandomForestClassifier accuracy: {}'.format(clf.score(X_test_tot, y_test_tot)))\n",
423 | "\n",
424 | "clf = GradientBoostingClassifier(random_state=34092)\n",
425 | "clf.fit(X_train_tot, y_train_tot)\n",
426 | "print('Base GradientBoostingClassifier accuracy: {}'.format(clf.score(X_test_tot, y_test_tot)))\n",
427 | "\n",
428 | "print('')\n",
429 | "\n",
430 | "cxpb = 0.5\n",
431 | "mutpb = 0.5\n",
432 | "ngen = 50\n",
433 | "verbose = True\n",
434 | "holdout_perf = {}\n",
435 | "\n",
436 | "logbook = tools.Logbook()\n",
437 | "logbook.header = ['gen', 'nevals'] + (stats.fields if stats else [])\n",
438 | "\n",
439 | "\n",
440 | "update_true_class_variance(population)\n",
441 | "\n",
442 | "# Evaluate the individuals with an invalid fitness ~\n",
443 | "invalid_ind = [ind for ind in population if not ind.fitness.valid]\n",
444 | "fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n",
445 | "for ind, fit in zip(invalid_ind, fitnesses):\n",
446 | " ind.fitness.values = fit\n",
447 | "\n",
448 | "if halloffame is not None:\n",
449 | " halloffame.update(population)\n",
450 | "\n",
451 | "record = stats.compile(population) if stats else {}\n",
452 | "logbook.record(gen=0, nevals=len(invalid_ind), **record)\n",
453 | "if verbose:\n",
454 | " print(logbook.stream)\n",
455 | "\n",
456 | "# Begin the generational process\n",
457 | "for gen in range(1, ngen + 1):\n",
458 | " # Select the next generation individuals\n",
459 | " offspring = toolbox.select(population, len(population))\n",
460 | "\n",
461 | " # Vary the pool of individuals\n",
462 | " offspring = algorithms.varAnd(offspring, toolbox, cxpb, mutpb)\n",
463 | "\n",
464 | " # Evaluate the individuals with an invalid fitness\n",
465 | " invalid_ind = [ind for ind in offspring if not ind.fitness.valid]\n",
466 | " fitnesses = toolbox.map(toolbox.evaluate, invalid_ind)\n",
467 | " for ind, fit in zip(invalid_ind, fitnesses):\n",
468 | " ind.fitness.values = fit\n",
469 | "\n",
470 | " # Update the hall of fame with the generated individuals\n",
471 | " if halloffame is not None:\n",
472 | " halloffame.update(offspring)\n",
473 | "\n",
474 | " # Replace the current population by the offspring\n",
475 | " population[:] = offspring\n",
476 | "\n",
477 | " #Generate true class predict_proba variance for each row.\n",
478 | " update_true_class_variance(population)\n",
479 | " \n",
480 | " # Append the current generation statistics to the logbook\n",
481 | " fits = pd.DataFrame()\n",
482 | " fits['score'] = map(lambda x: x.fitness.values[0], population)\n",
483 | " fits['contrib_variance'] = map(lambda x: x.fitness.values[1], population)\n",
484 | " \n",
485 | " record = stats.compile(population) if stats else {}\n",
486 | " logbook.record(gen=gen, nevals=len(invalid_ind),**record)\n",
487 | " holdout_perf[gen] = predict_holdout(population)\n",
488 | " if verbose:\n",
489 | " print(logbook.stream)\n",
490 | " print(fits.score.mean())\n",
491 | " print(fits.contrib_variance.mean())\n",
492 | " print(holdout_perf[gen])\n",
493 | "str(halloffame[0])"
494 | ]
495 | },
496 | {
497 | "cell_type": "code",
498 | "execution_count": 489,
499 | "metadata": {
500 | "collapsed": false
501 | },
502 | "outputs": [
503 | {
504 | "data": {
505 | "text/plain": [
506 | "deap.creator.FitnessMax((0.87240356083086057, 0.34103981606075923))"
507 | ]
508 | },
509 | "execution_count": 489,
510 | "metadata": {},
511 | "output_type": "execute_result"
512 | }
513 | ],
514 | "source": [
515 | "\n",
516 | "# pop = offspring[:]\n",
517 | "# pop = [halloffame[0]]\n",
518 | "halloffame[0].fitness"
519 | ]
520 | },
521 | {
522 | "cell_type": "code",
523 | "execution_count": 480,
524 | "metadata": {
525 | "collapsed": true
526 | },
527 | "outputs": [],
528 | "source": [
529 | "def predict_holdout(pop):\n",
530 | " forest_predictions = []\n",
531 | " subsample_sizes = []\n",
532 | " for ind_num, individual in enumerate(pop):\n",
533 | " func = toolbox.compile(expr=individual)\n",
534 | " # print individual\n",
535 | " subsample = np.array([func(*record) for record in X_train])\n",
536 | " subsample_sizes.append(subsample.sum())\n",
537 | "\n",
538 | " if X_train[subsample].shape[0] == 0:\n",
539 | " continue\n",
540 | "\n",
541 | " clf = DecisionTreeClassifier(random_state=34092)\n",
542 | " clf.fit(X_train[subsample], y_train[subsample])\n",
543 | " predictions = clf.predict(X_test_tot)\n",
544 | " forest_predictions.append(predictions)\n",
545 | " y_pred = np.array(\n",
546 | " [Counter(instance_forest_predictions).most_common(1)[0][0] for instance_forest_predictions in zip(*forest_predictions)])\n",
547 | " \n",
548 | " return np.sum(y_test_tot == y_pred)*1.0 / len(y_test_tot)"
549 | ]
550 | },
551 | {
552 | "cell_type": "code",
553 | "execution_count": 467,
554 | "metadata": {
555 | "collapsed": false
556 | },
557 | "outputs": [],
558 | "source": [
559 | "forest_predictions = []\n",
560 | "subsample_sizes = []\n",
561 | "for ind_num, individual in enumerate(pop):\n",
562 | " func = toolbox.compile(expr=individual)\n",
563 | "# print individual\n",
564 | " subsample = np.array([func(*record) for record in X_train])\n",
565 | " subsample_sizes.append(subsample.sum())\n",
566 | " \n",
567 | " if X_train[subsample].shape[0] == 0:\n",
568 | " continue\n",
569 | " \n",
570 | " clf = DecisionTreeClassifier(random_state=34092, max_depth=5)\n",
571 | " clf.fit(X_train[subsample], y_train[subsample])\n",
572 | " predictions = clf.predict(X_test_tot)\n",
573 | " forest_predictions.append(predictions)"
574 | ]
575 | },
576 | {
577 | "cell_type": "code",
578 | "execution_count": 468,
579 | "metadata": {
580 | "collapsed": true
581 | },
582 | "outputs": [],
583 | "source": [
584 | "# for x in \n",
585 | "fits = pd.DataFrame()\n",
586 | "fits['score'] = map(lambda x: x.fitness.values[0], pop)\n",
587 | "fits['contrib_variance'] = map(lambda x: x.fitness.values[1], pop)"
588 | ]
589 | },
590 | {
591 | "cell_type": "code",
592 | "execution_count": 469,
593 | "metadata": {
594 | "collapsed": false
595 | },
596 | "outputs": [],
597 | "source": [
598 | "%matplotlib inline\n",
599 | "import matplotlib.pyplot as plt\n",
600 | "# fits.contrib_variance.hist()"
601 | ]
602 | },
603 | {
604 | "cell_type": "code",
605 | "execution_count": 470,
606 | "metadata": {
607 | "collapsed": false
608 | },
609 | "outputs": [
610 | {
611 | "data": {
612 | "text/plain": [
613 | "0.78528189910979218"
614 | ]
615 | },
616 | "execution_count": 470,
617 | "metadata": {},
618 | "output_type": "execute_result"
619 | }
620 | ],
621 | "source": [
622 | "fits.score.mean()\n"
623 | ]
624 | },
625 | {
626 | "cell_type": "code",
627 | "execution_count": 471,
628 | "metadata": {
629 | "collapsed": false
630 | },
631 | "outputs": [
632 | {
633 | "name": "stdout",
634 | "output_type": "stream",
635 | "text": [
636 | "863.09\n",
637 | "100.248001975\n"
638 | ]
639 | }
640 | ],
641 | "source": [
642 | "\n",
643 | "# plt.hist(subsample_sizes)\n",
644 | "print np.mean(subsample_sizes)\n",
645 | "print np.std(subsample_sizes)"
646 | ]
647 | },
648 | {
649 | "cell_type": "code",
650 | "execution_count": 472,
651 | "metadata": {
652 | "collapsed": false
653 | },
654 | "outputs": [
655 | {
656 | "data": {
657 | "text/plain": [
658 | "2.3569844789356984"
659 | ]
660 | },
661 | "execution_count": 472,
662 | "metadata": {},
663 | "output_type": "execute_result"
664 | }
665 | ],
666 | "source": [
667 | "import pandas as pd\n",
668 | "## Mean # of unique labels predicted per sample.\n",
669 | "pd.DataFrame(forest_predictions).apply(lambda x: len(x.unique()), axis=0).mean()"
670 | ]
671 | },
672 | {
673 | "cell_type": "code",
674 | "execution_count": 473,
675 | "metadata": {
676 | "collapsed": false,
677 | "scrolled": false
678 | },
679 | "outputs": [
680 | {
681 | "data": {
682 | "text/plain": [
683 | "0.80487804878048785"
684 | ]
685 | },
686 | "execution_count": 473,
687 | "metadata": {},
688 | "output_type": "execute_result"
689 | }
690 | ],
691 | "source": [
692 | "from collections import Counter\n",
693 | "from sklearn.metrics import accuracy_score\n",
694 | "\n",
695 | "y_pred = np.array(\n",
696 | " [Counter(instance_forest_predictions).most_common(1)[0][0] for instance_forest_predictions in zip(*forest_predictions)])\n",
697 | "np.sum(y_test_tot == y_pred)*1.0 / len(y_test_tot)"
698 | ]
699 | },
700 | {
701 | "cell_type": "code",
702 | "execution_count": 465,
703 | "metadata": {
704 | "collapsed": false
705 | },
706 | "outputs": [
707 | {
708 | "data": {
709 | "text/html": [
710 | "\n",
711 | "
\n",
712 | " \n",
713 | " \n",
714 | " | \n",
715 | " 0 | \n",
716 | " 1 | \n",
717 | " 2 | \n",
718 | " 3 | \n",
719 | " 4 | \n",
720 | " 5 | \n",
721 | " 6 | \n",
722 | " 7 | \n",
723 | " 8 | \n",
724 | " 9 | \n",
725 | " ... | \n",
726 | " 441 | \n",
727 | " 442 | \n",
728 | " 443 | \n",
729 | " 444 | \n",
730 | " 445 | \n",
731 | " 446 | \n",
732 | " 447 | \n",
733 | " 448 | \n",
734 | " 449 | \n",
735 | " 450 | \n",
736 | "
\n",
737 | " \n",
738 | " \n",
739 | " \n",
740 | " | 0 | \n",
741 | " 6 | \n",
742 | " 6 | \n",
743 | " 1 | \n",
744 | " 0 | \n",
745 | " 1 | \n",
746 | " 8 | \n",
747 | " 2 | \n",
748 | " 2 | \n",
749 | " 7 | \n",
750 | " 4 | \n",
751 | " ... | \n",
752 | " 8 | \n",
753 | " 8 | \n",
754 | " 0 | \n",
755 | " 5 | \n",
756 | " 2 | \n",
757 | " 2 | \n",
758 | " 9 | \n",
759 | " 9 | \n",
760 | " 3 | \n",
761 | " 4 | \n",
762 | "
\n",
763 | " \n",
764 | "
\n",
765 | "
1 rows × 451 columns
\n",
766 | "
\n",
798 | "
\n",
799 | " \n",
800 | " \n",
801 | " | \n",
802 | " 0 | \n",
803 | " 1 | \n",
804 | " 2 | \n",
805 | " 3 | \n",
806 | " 4 | \n",
807 | " 5 | \n",
808 | " 6 | \n",
809 | " 7 | \n",
810 | " 8 | \n",
811 | " 9 | \n",
812 | " ... | \n",
813 | " 441 | \n",
814 | " 442 | \n",
815 | " 443 | \n",
816 | " 444 | \n",
817 | " 445 | \n",
818 | " 446 | \n",
819 | " 447 | \n",
820 | " 448 | \n",
821 | " 449 | \n",
822 | " 450 | \n",
823 | "
\n",
824 | " \n",
825 | " \n",
826 | " \n",
827 | " | 0 | \n",
828 | " 6 | \n",
829 | " 6 | \n",
830 | " 1 | \n",
831 | " 0 | \n",
832 | " 1 | \n",
833 | " 8 | \n",
834 | " 2 | \n",
835 | " 2 | \n",
836 | " 7 | \n",
837 | " 4 | \n",
838 | " ... | \n",
839 | " 8 | \n",
840 | " 8 | \n",
841 | " 0 | \n",
842 | " 5 | \n",
843 | " 2 | \n",
844 | " 2 | \n",
845 | " 9 | \n",
846 | " 9 | \n",
847 | " 3 | \n",
848 | " 4 | \n",
849 | "
\n",
850 | " \n",
851 | " | 1 | \n",
852 | " 6 | \n",
853 | " 6 | \n",
854 | " 1 | \n",
855 | " 0 | \n",
856 | " 1 | \n",
857 | " 8 | \n",
858 | " 2 | \n",
859 | " 2 | \n",
860 | " 7 | \n",
861 | " 4 | \n",
862 | " ... | \n",
863 | " 8 | \n",
864 | " 8 | \n",
865 | " 0 | \n",
866 | " 5 | \n",
867 | " 2 | \n",
868 | " 2 | \n",
869 | " 9 | \n",
870 | " 9 | \n",
871 | " 3 | \n",
872 | " 4 | \n",
873 | "
\n",
874 | " \n",
875 | " | 2 | \n",
876 | " 6 | \n",
877 | " 6 | \n",
878 | " 1 | \n",
879 | " 0 | \n",
880 | " 1 | \n",
881 | " 8 | \n",
882 | " 2 | \n",
883 | " 2 | \n",
884 | " 7 | \n",
885 | " 4 | \n",
886 | " ... | \n",
887 | " 8 | \n",
888 | " 8 | \n",
889 | " 0 | \n",
890 | " 5 | \n",
891 | " 2 | \n",
892 | " 2 | \n",
893 | " 9 | \n",
894 | " 9 | \n",
895 | " 3 | \n",
896 | " 4 | \n",
897 | "
\n",
898 | " \n",
899 | " | 3 | \n",
900 | " 6 | \n",
901 | " 6 | \n",
902 | " 1 | \n",
903 | " 0 | \n",
904 | " 1 | \n",
905 | " 8 | \n",
906 | " 2 | \n",
907 | " 2 | \n",
908 | " 7 | \n",
909 | " 4 | \n",
910 | " ... | \n",
911 | " 8 | \n",
912 | " 8 | \n",
913 | " 8 | \n",
914 | " 5 | \n",
915 | " 2 | \n",
916 | " 2 | \n",
917 | " 9 | \n",
918 | " 9 | \n",
919 | " 3 | \n",
920 | " 4 | \n",
921 | "
\n",
922 | " \n",
923 | " | 4 | \n",
924 | " 6 | \n",
925 | " 6 | \n",
926 | " 1 | \n",
927 | " 0 | \n",
928 | " 1 | \n",
929 | " 8 | \n",
930 | " 2 | \n",
931 | " 2 | \n",
932 | " 7 | \n",
933 | " 4 | \n",
934 | " ... | \n",
935 | " 8 | \n",
936 | " 8 | \n",
937 | " 0 | \n",
938 | " 5 | \n",
939 | " 2 | \n",
940 | " 2 | \n",
941 | " 9 | \n",
942 | " 9 | \n",
943 | " 3 | \n",
944 | " 4 | \n",
945 | "
\n",
946 | " \n",
947 | " | 5 | \n",
948 | " 6 | \n",
949 | " 6 | \n",
950 | " 1 | \n",
951 | " 0 | \n",
952 | " 1 | \n",
953 | " 8 | \n",
954 | " 2 | \n",
955 | " 2 | \n",
956 | " 7 | \n",
957 | " 4 | \n",
958 | " ... | \n",
959 | " 8 | \n",
960 | " 8 | \n",
961 | " 0 | \n",
962 | " 5 | \n",
963 | " 2 | \n",
964 | " 2 | \n",
965 | " 9 | \n",
966 | " 9 | \n",
967 | " 3 | \n",
968 | " 1 | \n",
969 | "
\n",
970 | " \n",
971 | " | 6 | \n",
972 | " 6 | \n",
973 | " 6 | \n",
974 | " 1 | \n",
975 | " 0 | \n",
976 | " 1 | \n",
977 | " 8 | \n",
978 | " 2 | \n",
979 | " 2 | \n",
980 | " 7 | \n",
981 | " 4 | \n",
982 | " ... | \n",
983 | " 8 | \n",
984 | " 8 | \n",
985 | " 0 | \n",
986 | " 5 | \n",
987 | " 2 | \n",
988 | " 2 | \n",
989 | " 9 | \n",
990 | " 9 | \n",
991 | " 3 | \n",
992 | " 4 | \n",
993 | "
\n",
994 | " \n",
995 | " | 7 | \n",
996 | " 6 | \n",
997 | " 6 | \n",
998 | " 1 | \n",
999 | " 0 | \n",
1000 | " 1 | \n",
1001 | " 8 | \n",
1002 | " 2 | \n",
1003 | " 2 | \n",
1004 | " 7 | \n",
1005 | " 4 | \n",
1006 | " ... | \n",
1007 | " 8 | \n",
1008 | " 8 | \n",
1009 | " 0 | \n",
1010 | " 5 | \n",
1011 | " 2 | \n",
1012 | " 2 | \n",
1013 | " 9 | \n",
1014 | " 9 | \n",
1015 | " 3 | \n",
1016 | " 4 | \n",
1017 | "
\n",
1018 | " \n",
1019 | " | 8 | \n",
1020 | " 6 | \n",
1021 | " 6 | \n",
1022 | " 1 | \n",
1023 | " 0 | \n",
1024 | " 1 | \n",
1025 | " 8 | \n",
1026 | " 2 | \n",
1027 | " 2 | \n",
1028 | " 7 | \n",
1029 | " 4 | \n",
1030 | " ... | \n",
1031 | " 8 | \n",
1032 | " 8 | \n",
1033 | " 0 | \n",
1034 | " 5 | \n",
1035 | " 2 | \n",
1036 | " 2 | \n",
1037 | " 9 | \n",
1038 | " 9 | \n",
1039 | " 3 | \n",
1040 | " 4 | \n",
1041 | "
\n",
1042 | " \n",
1043 | " | 9 | \n",
1044 | " 6 | \n",
1045 | " 6 | \n",
1046 | " 1 | \n",
1047 | " 0 | \n",
1048 | " 1 | \n",
1049 | " 8 | \n",
1050 | " 2 | \n",
1051 | " 2 | \n",
1052 | " 7 | \n",
1053 | " 4 | \n",
1054 | " ... | \n",
1055 | " 8 | \n",
1056 | " 8 | \n",
1057 | " 0 | \n",
1058 | " 5 | \n",
1059 | " 2 | \n",
1060 | " 2 | \n",
1061 | " 9 | \n",
1062 | " 9 | \n",
1063 | " 3 | \n",
1064 | " 1 | \n",
1065 | "
\n",
1066 | " \n",
1067 | " | 10 | \n",
1068 | " 6 | \n",
1069 | " 6 | \n",
1070 | " 1 | \n",
1071 | " 0 | \n",
1072 | " 1 | \n",
1073 | " 8 | \n",
1074 | " 2 | \n",
1075 | " 2 | \n",
1076 | " 7 | \n",
1077 | " 4 | \n",
1078 | " ... | \n",
1079 | " 8 | \n",
1080 | " 8 | \n",
1081 | " 0 | \n",
1082 | " 5 | \n",
1083 | " 2 | \n",
1084 | " 2 | \n",
1085 | " 9 | \n",
1086 | " 9 | \n",
1087 | " 3 | \n",
1088 | " 1 | \n",
1089 | "
\n",
1090 | " \n",
1091 | " | 11 | \n",
1092 | " 6 | \n",
1093 | " 6 | \n",
1094 | " 1 | \n",
1095 | " 0 | \n",
1096 | " 1 | \n",
1097 | " 8 | \n",
1098 | " 2 | \n",
1099 | " 7 | \n",
1100 | " 7 | \n",
1101 | " 4 | \n",
1102 | " ... | \n",
1103 | " 8 | \n",
1104 | " 8 | \n",
1105 | " 0 | \n",
1106 | " 5 | \n",
1107 | " 2 | \n",
1108 | " 2 | \n",
1109 | " 9 | \n",
1110 | " 9 | \n",
1111 | " 3 | \n",
1112 | " 4 | \n",
1113 | "
\n",
1114 | " \n",
1115 | " | 12 | \n",
1116 | " 6 | \n",
1117 | " 6 | \n",
1118 | " 1 | \n",
1119 | " 0 | \n",
1120 | " 1 | \n",
1121 | " 8 | \n",
1122 | " 2 | \n",
1123 | " 2 | \n",
1124 | " 7 | \n",
1125 | " 4 | \n",
1126 | " ... | \n",
1127 | " 8 | \n",
1128 | " 8 | \n",
1129 | " 0 | \n",
1130 | " 5 | \n",
1131 | " 2 | \n",
1132 | " 2 | \n",
1133 | " 9 | \n",
1134 | " 9 | \n",
1135 | " 3 | \n",
1136 | " 4 | \n",
1137 | "
\n",
1138 | " \n",
1139 | " | 13 | \n",
1140 | " 6 | \n",
1141 | " 6 | \n",
1142 | " 1 | \n",
1143 | " 0 | \n",
1144 | " 1 | \n",
1145 | " 8 | \n",
1146 | " 2 | \n",
1147 | " 2 | \n",
1148 | " 7 | \n",
1149 | " 4 | \n",
1150 | " ... | \n",
1151 | " 8 | \n",
1152 | " 8 | \n",
1153 | " 0 | \n",
1154 | " 5 | \n",
1155 | " 2 | \n",
1156 | " 2 | \n",
1157 | " 9 | \n",
1158 | " 9 | \n",
1159 | " 3 | \n",
1160 | " 4 | \n",
1161 | "
\n",
1162 | " \n",
1163 | " | 14 | \n",
1164 | " 6 | \n",
1165 | " 6 | \n",
1166 | " 1 | \n",
1167 | " 0 | \n",
1168 | " 1 | \n",
1169 | " 8 | \n",
1170 | " 2 | \n",
1171 | " 2 | \n",
1172 | " 7 | \n",
1173 | " 4 | \n",
1174 | " ... | \n",
1175 | " 8 | \n",
1176 | " 8 | \n",
1177 | " 0 | \n",
1178 | " 5 | \n",
1179 | " 2 | \n",
1180 | " 2 | \n",
1181 | " 9 | \n",
1182 | " 9 | \n",
1183 | " 3 | \n",
1184 | " 4 | \n",
1185 | "
\n",
1186 | " \n",
1187 | " | 15 | \n",
1188 | " 6 | \n",
1189 | " 6 | \n",
1190 | " 1 | \n",
1191 | " 0 | \n",
1192 | " 1 | \n",
1193 | " 8 | \n",
1194 | " 2 | \n",
1195 | " 2 | \n",
1196 | " 7 | \n",
1197 | " 4 | \n",
1198 | " ... | \n",
1199 | " 8 | \n",
1200 | " 8 | \n",
1201 | " 0 | \n",
1202 | " 5 | \n",
1203 | " 2 | \n",
1204 | " 2 | \n",
1205 | " 9 | \n",
1206 | " 9 | \n",
1207 | " 3 | \n",
1208 | " 4 | \n",
1209 | "
\n",
1210 | " \n",
1211 | " | 16 | \n",
1212 | " 6 | \n",
1213 | " 6 | \n",
1214 | " 1 | \n",
1215 | " 0 | \n",
1216 | " 1 | \n",
1217 | " 8 | \n",
1218 | " 2 | \n",
1219 | " 2 | \n",
1220 | " 7 | \n",
1221 | " 4 | \n",
1222 | " ... | \n",
1223 | " 8 | \n",
1224 | " 8 | \n",
1225 | " 0 | \n",
1226 | " 5 | \n",
1227 | " 2 | \n",
1228 | " 2 | \n",
1229 | " 9 | \n",
1230 | " 9 | \n",
1231 | " 3 | \n",
1232 | " 4 | \n",
1233 | "
\n",
1234 | " \n",
1235 | " | 17 | \n",
1236 | " 6 | \n",
1237 | " 6 | \n",
1238 | " 1 | \n",
1239 | " 0 | \n",
1240 | " 1 | \n",
1241 | " 8 | \n",
1242 | " 2 | \n",
1243 | " 2 | \n",
1244 | " 7 | \n",
1245 | " 4 | \n",
1246 | " ... | \n",
1247 | " 8 | \n",
1248 | " 8 | \n",
1249 | " 0 | \n",
1250 | " 5 | \n",
1251 | " 2 | \n",
1252 | " 2 | \n",
1253 | " 9 | \n",
1254 | " 9 | \n",
1255 | " 3 | \n",
1256 | " 1 | \n",
1257 | "
\n",
1258 | " \n",
1259 | " | 18 | \n",
1260 | " 6 | \n",
1261 | " 6 | \n",
1262 | " 1 | \n",
1263 | " 0 | \n",
1264 | " 1 | \n",
1265 | " 8 | \n",
1266 | " 2 | \n",
1267 | " 2 | \n",
1268 | " 7 | \n",
1269 | " 4 | \n",
1270 | " ... | \n",
1271 | " 8 | \n",
1272 | " 8 | \n",
1273 | " 0 | \n",
1274 | " 5 | \n",
1275 | " 2 | \n",
1276 | " 2 | \n",
1277 | " 9 | \n",
1278 | " 9 | \n",
1279 | " 3 | \n",
1280 | " 4 | \n",
1281 | "
\n",
1282 | " \n",
1283 | " | 19 | \n",
1284 | " 6 | \n",
1285 | " 6 | \n",
1286 | " 1 | \n",
1287 | " 0 | \n",
1288 | " 1 | \n",
1289 | " 8 | \n",
1290 | " 2 | \n",
1291 | " 2 | \n",
1292 | " 7 | \n",
1293 | " 4 | \n",
1294 | " ... | \n",
1295 | " 8 | \n",
1296 | " 8 | \n",
1297 | " 0 | \n",
1298 | " 5 | \n",
1299 | " 2 | \n",
1300 | " 2 | \n",
1301 | " 9 | \n",
1302 | " 9 | \n",
1303 | " 3 | \n",
1304 | " 4 | \n",
1305 | "
\n",
1306 | " \n",
1307 | " | 20 | \n",
1308 | " 6 | \n",
1309 | " 6 | \n",
1310 | " 1 | \n",
1311 | " 0 | \n",
1312 | " 1 | \n",
1313 | " 8 | \n",
1314 | " 2 | \n",
1315 | " 2 | \n",
1316 | " 7 | \n",
1317 | " 4 | \n",
1318 | " ... | \n",
1319 | " 8 | \n",
1320 | " 8 | \n",
1321 | " 0 | \n",
1322 | " 5 | \n",
1323 | " 2 | \n",
1324 | " 2 | \n",
1325 | " 9 | \n",
1326 | " 9 | \n",
1327 | " 3 | \n",
1328 | " 4 | \n",
1329 | "
\n",
1330 | " \n",
1331 | " | 21 | \n",
1332 | " 6 | \n",
1333 | " 6 | \n",
1334 | " 1 | \n",
1335 | " 0 | \n",
1336 | " 1 | \n",
1337 | " 8 | \n",
1338 | " 2 | \n",
1339 | " 2 | \n",
1340 | " 7 | \n",
1341 | " 4 | \n",
1342 | " ... | \n",
1343 | " 8 | \n",
1344 | " 8 | \n",
1345 | " 0 | \n",
1346 | " 5 | \n",
1347 | " 2 | \n",
1348 | " 2 | \n",
1349 | " 9 | \n",
1350 | " 9 | \n",
1351 | " 3 | \n",
1352 | " 4 | \n",
1353 | "
\n",
1354 | " \n",
1355 | " | 22 | \n",
1356 | " 5 | \n",
1357 | " 6 | \n",
1358 | " 8 | \n",
1359 | " 0 | \n",
1360 | " 8 | \n",
1361 | " 8 | \n",
1362 | " 8 | \n",
1363 | " 5 | \n",
1364 | " 7 | \n",
1365 | " 4 | \n",
1366 | " ... | \n",
1367 | " 8 | \n",
1368 | " 8 | \n",
1369 | " 6 | \n",
1370 | " 5 | \n",
1371 | " 1 | \n",
1372 | " 8 | \n",
1373 | " 9 | \n",
1374 | " 9 | \n",
1375 | " 5 | \n",
1376 | " 5 | \n",
1377 | "
\n",
1378 | " \n",
1379 | " | 23 | \n",
1380 | " 6 | \n",
1381 | " 6 | \n",
1382 | " 1 | \n",
1383 | " 0 | \n",
1384 | " 1 | \n",
1385 | " 8 | \n",
1386 | " 2 | \n",
1387 | " 2 | \n",
1388 | " 7 | \n",
1389 | " 4 | \n",
1390 | " ... | \n",
1391 | " 8 | \n",
1392 | " 8 | \n",
1393 | " 0 | \n",
1394 | " 5 | \n",
1395 | " 2 | \n",
1396 | " 2 | \n",
1397 | " 9 | \n",
1398 | " 9 | \n",
1399 | " 3 | \n",
1400 | " 4 | \n",
1401 | "
\n",
1402 | " \n",
1403 | " | 24 | \n",
1404 | " 6 | \n",
1405 | " 6 | \n",
1406 | " 1 | \n",
1407 | " 0 | \n",
1408 | " 1 | \n",
1409 | " 8 | \n",
1410 | " 2 | \n",
1411 | " 2 | \n",
1412 | " 7 | \n",
1413 | " 4 | \n",
1414 | " ... | \n",
1415 | " 8 | \n",
1416 | " 8 | \n",
1417 | " 0 | \n",
1418 | " 5 | \n",
1419 | " 2 | \n",
1420 | " 2 | \n",
1421 | " 9 | \n",
1422 | " 9 | \n",
1423 | " 3 | \n",
1424 | " 4 | \n",
1425 | "
\n",
1426 | " \n",
1427 | " | 25 | \n",
1428 | " 6 | \n",
1429 | " 6 | \n",
1430 | " 1 | \n",
1431 | " 0 | \n",
1432 | " 1 | \n",
1433 | " 8 | \n",
1434 | " 2 | \n",
1435 | " 2 | \n",
1436 | " 7 | \n",
1437 | " 4 | \n",
1438 | " ... | \n",
1439 | " 8 | \n",
1440 | " 8 | \n",
1441 | " 0 | \n",
1442 | " 5 | \n",
1443 | " 2 | \n",
1444 | " 2 | \n",
1445 | " 9 | \n",
1446 | " 9 | \n",
1447 | " 3 | \n",
1448 | " 4 | \n",
1449 | "
\n",
1450 | " \n",
1451 | " | 26 | \n",
1452 | " 6 | \n",
1453 | " 6 | \n",
1454 | " 1 | \n",
1455 | " 0 | \n",
1456 | " 1 | \n",
1457 | " 8 | \n",
1458 | " 2 | \n",
1459 | " 2 | \n",
1460 | " 7 | \n",
1461 | " 4 | \n",
1462 | " ... | \n",
1463 | " 8 | \n",
1464 | " 8 | \n",
1465 | " 0 | \n",
1466 | " 5 | \n",
1467 | " 2 | \n",
1468 | " 2 | \n",
1469 | " 9 | \n",
1470 | " 9 | \n",
1471 | " 3 | \n",
1472 | " 4 | \n",
1473 | "
\n",
1474 | " \n",
1475 | " | 27 | \n",
1476 | " 6 | \n",
1477 | " 6 | \n",
1478 | " 1 | \n",
1479 | " 0 | \n",
1480 | " 1 | \n",
1481 | " 8 | \n",
1482 | " 2 | \n",
1483 | " 2 | \n",
1484 | " 7 | \n",
1485 | " 4 | \n",
1486 | " ... | \n",
1487 | " 8 | \n",
1488 | " 8 | \n",
1489 | " 0 | \n",
1490 | " 5 | \n",
1491 | " 2 | \n",
1492 | " 2 | \n",
1493 | " 9 | \n",
1494 | " 9 | \n",
1495 | " 3 | \n",
1496 | " 4 | \n",
1497 | "
\n",
1498 | " \n",
1499 | " | 28 | \n",
1500 | " 6 | \n",
1501 | " 6 | \n",
1502 | " 1 | \n",
1503 | " 0 | \n",
1504 | " 1 | \n",
1505 | " 8 | \n",
1506 | " 2 | \n",
1507 | " 2 | \n",
1508 | " 7 | \n",
1509 | " 4 | \n",
1510 | " ... | \n",
1511 | " 8 | \n",
1512 | " 8 | \n",
1513 | " 0 | \n",
1514 | " 5 | \n",
1515 | " 2 | \n",
1516 | " 2 | \n",
1517 | " 9 | \n",
1518 | " 9 | \n",
1519 | " 3 | \n",
1520 | " 4 | \n",
1521 | "
\n",
1522 | " \n",
1523 | " | 29 | \n",
1524 | " 6 | \n",
1525 | " 6 | \n",
1526 | " 1 | \n",
1527 | " 0 | \n",
1528 | " 1 | \n",
1529 | " 8 | \n",
1530 | " 2 | \n",
1531 | " 2 | \n",
1532 | " 7 | \n",
1533 | " 4 | \n",
1534 | " ... | \n",
1535 | " 8 | \n",
1536 | " 8 | \n",
1537 | " 0 | \n",
1538 | " 5 | \n",
1539 | " 2 | \n",
1540 | " 2 | \n",
1541 | " 9 | \n",
1542 | " 9 | \n",
1543 | " 3 | \n",
1544 | " 4 | \n",
1545 | "
\n",
1546 | " \n",
1547 | " | ... | \n",
1548 | " ... | \n",
1549 | " ... | \n",
1550 | " ... | \n",
1551 | " ... | \n",
1552 | " ... | \n",
1553 | " ... | \n",
1554 | " ... | \n",
1555 | " ... | \n",
1556 | " ... | \n",
1557 | " ... | \n",
1558 | " ... | \n",
1559 | " ... | \n",
1560 | " ... | \n",
1561 | " ... | \n",
1562 | " ... | \n",
1563 | " ... | \n",
1564 | " ... | \n",
1565 | " ... | \n",
1566 | " ... | \n",
1567 | " ... | \n",
1568 | " ... | \n",
1569 | "
\n",
1570 | " \n",
1571 | " | 69 | \n",
1572 | " 6 | \n",
1573 | " 6 | \n",
1574 | " 1 | \n",
1575 | " 0 | \n",
1576 | " 1 | \n",
1577 | " 8 | \n",
1578 | " 2 | \n",
1579 | " 2 | \n",
1580 | " 7 | \n",
1581 | " 4 | \n",
1582 | " ... | \n",
1583 | " 8 | \n",
1584 | " 8 | \n",
1585 | " 0 | \n",
1586 | " 5 | \n",
1587 | " 2 | \n",
1588 | " 2 | \n",
1589 | " 9 | \n",
1590 | " 9 | \n",
1591 | " 3 | \n",
1592 | " 4 | \n",
1593 | "
\n",
1594 | " \n",
1595 | " | 70 | \n",
1596 | " 6 | \n",
1597 | " 6 | \n",
1598 | " 1 | \n",
1599 | " 0 | \n",
1600 | " 1 | \n",
1601 | " 8 | \n",
1602 | " 2 | \n",
1603 | " 2 | \n",
1604 | " 7 | \n",
1605 | " 4 | \n",
1606 | " ... | \n",
1607 | " 8 | \n",
1608 | " 8 | \n",
1609 | " 0 | \n",
1610 | " 5 | \n",
1611 | " 2 | \n",
1612 | " 2 | \n",
1613 | " 9 | \n",
1614 | " 9 | \n",
1615 | " 3 | \n",
1616 | " 4 | \n",
1617 | "
\n",
1618 | " \n",
1619 | " | 71 | \n",
1620 | " 6 | \n",
1621 | " 6 | \n",
1622 | " 1 | \n",
1623 | " 0 | \n",
1624 | " 1 | \n",
1625 | " 8 | \n",
1626 | " 2 | \n",
1627 | " 2 | \n",
1628 | " 7 | \n",
1629 | " 4 | \n",
1630 | " ... | \n",
1631 | " 8 | \n",
1632 | " 8 | \n",
1633 | " 0 | \n",
1634 | " 5 | \n",
1635 | " 2 | \n",
1636 | " 2 | \n",
1637 | " 9 | \n",
1638 | " 9 | \n",
1639 | " 3 | \n",
1640 | " 4 | \n",
1641 | "
\n",
1642 | " \n",
1643 | " | 72 | \n",
1644 | " 5 | \n",
1645 | " 6 | \n",
1646 | " 8 | \n",
1647 | " 0 | \n",
1648 | " 1 | \n",
1649 | " 8 | \n",
1650 | " 8 | \n",
1651 | " 8 | \n",
1652 | " 7 | \n",
1653 | " 4 | \n",
1654 | " ... | \n",
1655 | " 8 | \n",
1656 | " 8 | \n",
1657 | " 7 | \n",
1658 | " 5 | \n",
1659 | " 1 | \n",
1660 | " 8 | \n",
1661 | " 0 | \n",
1662 | " 9 | \n",
1663 | " 1 | \n",
1664 | " 5 | \n",
1665 | "
\n",
1666 | " \n",
1667 | " | 73 | \n",
1668 | " 6 | \n",
1669 | " 6 | \n",
1670 | " 1 | \n",
1671 | " 0 | \n",
1672 | " 1 | \n",
1673 | " 8 | \n",
1674 | " 2 | \n",
1675 | " 2 | \n",
1676 | " 7 | \n",
1677 | " 4 | \n",
1678 | " ... | \n",
1679 | " 8 | \n",
1680 | " 8 | \n",
1681 | " 0 | \n",
1682 | " 5 | \n",
1683 | " 2 | \n",
1684 | " 2 | \n",
1685 | " 9 | \n",
1686 | " 9 | \n",
1687 | " 3 | \n",
1688 | " 4 | \n",
1689 | "
\n",
1690 | " \n",
1691 | " | 74 | \n",
1692 | " 6 | \n",
1693 | " 6 | \n",
1694 | " 1 | \n",
1695 | " 0 | \n",
1696 | " 1 | \n",
1697 | " 8 | \n",
1698 | " 2 | \n",
1699 | " 2 | \n",
1700 | " 7 | \n",
1701 | " 4 | \n",
1702 | " ... | \n",
1703 | " 8 | \n",
1704 | " 8 | \n",
1705 | " 0 | \n",
1706 | " 5 | \n",
1707 | " 2 | \n",
1708 | " 2 | \n",
1709 | " 9 | \n",
1710 | " 9 | \n",
1711 | " 3 | \n",
1712 | " 4 | \n",
1713 | "
\n",
1714 | " \n",
1715 | " | 75 | \n",
1716 | " 6 | \n",
1717 | " 6 | \n",
1718 | " 1 | \n",
1719 | " 0 | \n",
1720 | " 1 | \n",
1721 | " 8 | \n",
1722 | " 2 | \n",
1723 | " 2 | \n",
1724 | " 7 | \n",
1725 | " 4 | \n",
1726 | " ... | \n",
1727 | " 8 | \n",
1728 | " 8 | \n",
1729 | " 0 | \n",
1730 | " 5 | \n",
1731 | " 2 | \n",
1732 | " 2 | \n",
1733 | " 9 | \n",
1734 | " 9 | \n",
1735 | " 3 | \n",
1736 | " 4 | \n",
1737 | "
\n",
1738 | " \n",
1739 | " | 76 | \n",
1740 | " 6 | \n",
1741 | " 6 | \n",
1742 | " 1 | \n",
1743 | " 0 | \n",
1744 | " 1 | \n",
1745 | " 8 | \n",
1746 | " 2 | \n",
1747 | " 2 | \n",
1748 | " 7 | \n",
1749 | " 4 | \n",
1750 | " ... | \n",
1751 | " 8 | \n",
1752 | " 8 | \n",
1753 | " 0 | \n",
1754 | " 5 | \n",
1755 | " 2 | \n",
1756 | " 2 | \n",
1757 | " 9 | \n",
1758 | " 9 | \n",
1759 | " 3 | \n",
1760 | " 4 | \n",
1761 | "
\n",
1762 | " \n",
1763 | " | 77 | \n",
1764 | " 6 | \n",
1765 | " 6 | \n",
1766 | " 1 | \n",
1767 | " 0 | \n",
1768 | " 1 | \n",
1769 | " 8 | \n",
1770 | " 2 | \n",
1771 | " 2 | \n",
1772 | " 7 | \n",
1773 | " 4 | \n",
1774 | " ... | \n",
1775 | " 8 | \n",
1776 | " 8 | \n",
1777 | " 0 | \n",
1778 | " 5 | \n",
1779 | " 2 | \n",
1780 | " 2 | \n",
1781 | " 9 | \n",
1782 | " 9 | \n",
1783 | " 3 | \n",
1784 | " 4 | \n",
1785 | "
\n",
1786 | " \n",
1787 | " | 78 | \n",
1788 | " 6 | \n",
1789 | " 6 | \n",
1790 | " 1 | \n",
1791 | " 0 | \n",
1792 | " 1 | \n",
1793 | " 8 | \n",
1794 | " 2 | \n",
1795 | " 2 | \n",
1796 | " 7 | \n",
1797 | " 4 | \n",
1798 | " ... | \n",
1799 | " 8 | \n",
1800 | " 8 | \n",
1801 | " 0 | \n",
1802 | " 5 | \n",
1803 | " 2 | \n",
1804 | " 2 | \n",
1805 | " 9 | \n",
1806 | " 9 | \n",
1807 | " 3 | \n",
1808 | " 4 | \n",
1809 | "
\n",
1810 | " \n",
1811 | " | 79 | \n",
1812 | " 6 | \n",
1813 | " 6 | \n",
1814 | " 1 | \n",
1815 | " 0 | \n",
1816 | " 1 | \n",
1817 | " 8 | \n",
1818 | " 2 | \n",
1819 | " 2 | \n",
1820 | " 7 | \n",
1821 | " 4 | \n",
1822 | " ... | \n",
1823 | " 8 | \n",
1824 | " 8 | \n",
1825 | " 0 | \n",
1826 | " 5 | \n",
1827 | " 2 | \n",
1828 | " 2 | \n",
1829 | " 9 | \n",
1830 | " 9 | \n",
1831 | " 3 | \n",
1832 | " 4 | \n",
1833 | "
\n",
1834 | " \n",
1835 | " | 80 | \n",
1836 | " 6 | \n",
1837 | " 6 | \n",
1838 | " 1 | \n",
1839 | " 0 | \n",
1840 | " 1 | \n",
1841 | " 8 | \n",
1842 | " 2 | \n",
1843 | " 2 | \n",
1844 | " 7 | \n",
1845 | " 4 | \n",
1846 | " ... | \n",
1847 | " 8 | \n",
1848 | " 8 | \n",
1849 | " 0 | \n",
1850 | " 5 | \n",
1851 | " 2 | \n",
1852 | " 2 | \n",
1853 | " 9 | \n",
1854 | " 9 | \n",
1855 | " 3 | \n",
1856 | " 4 | \n",
1857 | "
\n",
1858 | " \n",
1859 | " | 81 | \n",
1860 | " 6 | \n",
1861 | " 6 | \n",
1862 | " 1 | \n",
1863 | " 0 | \n",
1864 | " 1 | \n",
1865 | " 8 | \n",
1866 | " 2 | \n",
1867 | " 2 | \n",
1868 | " 7 | \n",
1869 | " 4 | \n",
1870 | " ... | \n",
1871 | " 8 | \n",
1872 | " 8 | \n",
1873 | " 0 | \n",
1874 | " 5 | \n",
1875 | " 2 | \n",
1876 | " 2 | \n",
1877 | " 9 | \n",
1878 | " 9 | \n",
1879 | " 3 | \n",
1880 | " 4 | \n",
1881 | "
\n",
1882 | " \n",
1883 | " | 82 | \n",
1884 | " 6 | \n",
1885 | " 6 | \n",
1886 | " 1 | \n",
1887 | " 0 | \n",
1888 | " 1 | \n",
1889 | " 8 | \n",
1890 | " 2 | \n",
1891 | " 2 | \n",
1892 | " 7 | \n",
1893 | " 4 | \n",
1894 | " ... | \n",
1895 | " 8 | \n",
1896 | " 8 | \n",
1897 | " 0 | \n",
1898 | " 5 | \n",
1899 | " 2 | \n",
1900 | " 2 | \n",
1901 | " 9 | \n",
1902 | " 9 | \n",
1903 | " 3 | \n",
1904 | " 4 | \n",
1905 | "
\n",
1906 | " \n",
1907 | " | 83 | \n",
1908 | " 6 | \n",
1909 | " 6 | \n",
1910 | " 1 | \n",
1911 | " 0 | \n",
1912 | " 1 | \n",
1913 | " 8 | \n",
1914 | " 2 | \n",
1915 | " 2 | \n",
1916 | " 7 | \n",
1917 | " 4 | \n",
1918 | " ... | \n",
1919 | " 8 | \n",
1920 | " 8 | \n",
1921 | " 0 | \n",
1922 | " 5 | \n",
1923 | " 2 | \n",
1924 | " 2 | \n",
1925 | " 9 | \n",
1926 | " 9 | \n",
1927 | " 3 | \n",
1928 | " 4 | \n",
1929 | "
\n",
1930 | " \n",
1931 | " | 84 | \n",
1932 | " 6 | \n",
1933 | " 6 | \n",
1934 | " 8 | \n",
1935 | " 0 | \n",
1936 | " 1 | \n",
1937 | " 3 | \n",
1938 | " 8 | \n",
1939 | " 7 | \n",
1940 | " 7 | \n",
1941 | " 1 | \n",
1942 | " ... | \n",
1943 | " 8 | \n",
1944 | " 8 | \n",
1945 | " 1 | \n",
1946 | " 5 | \n",
1947 | " 2 | \n",
1948 | " 8 | \n",
1949 | " 3 | \n",
1950 | " 9 | \n",
1951 | " 3 | \n",
1952 | " 1 | \n",
1953 | "
\n",
1954 | " \n",
1955 | " | 85 | \n",
1956 | " 6 | \n",
1957 | " 6 | \n",
1958 | " 8 | \n",
1959 | " 0 | \n",
1960 | " 1 | \n",
1961 | " 9 | \n",
1962 | " 8 | \n",
1963 | " 5 | \n",
1964 | " 7 | \n",
1965 | " 4 | \n",
1966 | " ... | \n",
1967 | " 8 | \n",
1968 | " 8 | \n",
1969 | " 4 | \n",
1970 | " 5 | \n",
1971 | " 2 | \n",
1972 | " 8 | \n",
1973 | " 9 | \n",
1974 | " 9 | \n",
1975 | " 5 | \n",
1976 | " 1 | \n",
1977 | "
\n",
1978 | " \n",
1979 | " | 86 | \n",
1980 | " 6 | \n",
1981 | " 6 | \n",
1982 | " 1 | \n",
1983 | " 0 | \n",
1984 | " 1 | \n",
1985 | " 8 | \n",
1986 | " 2 | \n",
1987 | " 2 | \n",
1988 | " 7 | \n",
1989 | " 4 | \n",
1990 | " ... | \n",
1991 | " 8 | \n",
1992 | " 8 | \n",
1993 | " 0 | \n",
1994 | " 5 | \n",
1995 | " 2 | \n",
1996 | " 2 | \n",
1997 | " 9 | \n",
1998 | " 9 | \n",
1999 | " 3 | \n",
2000 | " 4 | \n",
2001 | "
\n",
2002 | " \n",
2003 | " | 87 | \n",
2004 | " 6 | \n",
2005 | " 6 | \n",
2006 | " 1 | \n",
2007 | " 0 | \n",
2008 | " 1 | \n",
2009 | " 8 | \n",
2010 | " 2 | \n",
2011 | " 2 | \n",
2012 | " 7 | \n",
2013 | " 4 | \n",
2014 | " ... | \n",
2015 | " 8 | \n",
2016 | " 8 | \n",
2017 | " 0 | \n",
2018 | " 5 | \n",
2019 | " 2 | \n",
2020 | " 2 | \n",
2021 | " 9 | \n",
2022 | " 9 | \n",
2023 | " 3 | \n",
2024 | " 4 | \n",
2025 | "
\n",
2026 | " \n",
2027 | " | 88 | \n",
2028 | " 6 | \n",
2029 | " 6 | \n",
2030 | " 1 | \n",
2031 | " 0 | \n",
2032 | " 1 | \n",
2033 | " 8 | \n",
2034 | " 2 | \n",
2035 | " 2 | \n",
2036 | " 7 | \n",
2037 | " 4 | \n",
2038 | " ... | \n",
2039 | " 8 | \n",
2040 | " 8 | \n",
2041 | " 0 | \n",
2042 | " 5 | \n",
2043 | " 2 | \n",
2044 | " 2 | \n",
2045 | " 9 | \n",
2046 | " 9 | \n",
2047 | " 3 | \n",
2048 | " 4 | \n",
2049 | "
\n",
2050 | " \n",
2051 | " | 89 | \n",
2052 | " 6 | \n",
2053 | " 2 | \n",
2054 | " 1 | \n",
2055 | " 0 | \n",
2056 | " 1 | \n",
2057 | " 1 | \n",
2058 | " 2 | \n",
2059 | " 7 | \n",
2060 | " 7 | \n",
2061 | " 4 | \n",
2062 | " ... | \n",
2063 | " 1 | \n",
2064 | " 1 | \n",
2065 | " 5 | \n",
2066 | " 5 | \n",
2067 | " 2 | \n",
2068 | " 2 | \n",
2069 | " 9 | \n",
2070 | " 9 | \n",
2071 | " 5 | \n",
2072 | " 2 | \n",
2073 | "
\n",
2074 | " \n",
2075 | " | 90 | \n",
2076 | " 6 | \n",
2077 | " 6 | \n",
2078 | " 1 | \n",
2079 | " 0 | \n",
2080 | " 1 | \n",
2081 | " 8 | \n",
2082 | " 2 | \n",
2083 | " 2 | \n",
2084 | " 7 | \n",
2085 | " 4 | \n",
2086 | " ... | \n",
2087 | " 8 | \n",
2088 | " 8 | \n",
2089 | " 0 | \n",
2090 | " 5 | \n",
2091 | " 2 | \n",
2092 | " 2 | \n",
2093 | " 9 | \n",
2094 | " 9 | \n",
2095 | " 3 | \n",
2096 | " 4 | \n",
2097 | "
\n",
2098 | " \n",
2099 | " | 91 | \n",
2100 | " 6 | \n",
2101 | " 6 | \n",
2102 | " 1 | \n",
2103 | " 0 | \n",
2104 | " 1 | \n",
2105 | " 8 | \n",
2106 | " 2 | \n",
2107 | " 2 | \n",
2108 | " 7 | \n",
2109 | " 4 | \n",
2110 | " ... | \n",
2111 | " 8 | \n",
2112 | " 8 | \n",
2113 | " 0 | \n",
2114 | " 5 | \n",
2115 | " 2 | \n",
2116 | " 2 | \n",
2117 | " 9 | \n",
2118 | " 9 | \n",
2119 | " 3 | \n",
2120 | " 4 | \n",
2121 | "
\n",
2122 | " \n",
2123 | " | 92 | \n",
2124 | " 6 | \n",
2125 | " 6 | \n",
2126 | " 1 | \n",
2127 | " 0 | \n",
2128 | " 1 | \n",
2129 | " 8 | \n",
2130 | " 2 | \n",
2131 | " 2 | \n",
2132 | " 7 | \n",
2133 | " 4 | \n",
2134 | " ... | \n",
2135 | " 8 | \n",
2136 | " 8 | \n",
2137 | " 0 | \n",
2138 | " 5 | \n",
2139 | " 2 | \n",
2140 | " 2 | \n",
2141 | " 9 | \n",
2142 | " 9 | \n",
2143 | " 3 | \n",
2144 | " 4 | \n",
2145 | "
\n",
2146 | " \n",
2147 | " | 93 | \n",
2148 | " 6 | \n",
2149 | " 6 | \n",
2150 | " 1 | \n",
2151 | " 0 | \n",
2152 | " 1 | \n",
2153 | " 8 | \n",
2154 | " 2 | \n",
2155 | " 2 | \n",
2156 | " 7 | \n",
2157 | " 4 | \n",
2158 | " ... | \n",
2159 | " 8 | \n",
2160 | " 8 | \n",
2161 | " 0 | \n",
2162 | " 5 | \n",
2163 | " 2 | \n",
2164 | " 2 | \n",
2165 | " 9 | \n",
2166 | " 9 | \n",
2167 | " 3 | \n",
2168 | " 4 | \n",
2169 | "
\n",
2170 | " \n",
2171 | " | 94 | \n",
2172 | " 6 | \n",
2173 | " 6 | \n",
2174 | " 1 | \n",
2175 | " 0 | \n",
2176 | " 1 | \n",
2177 | " 8 | \n",
2178 | " 2 | \n",
2179 | " 2 | \n",
2180 | " 7 | \n",
2181 | " 4 | \n",
2182 | " ... | \n",
2183 | " 8 | \n",
2184 | " 8 | \n",
2185 | " 0 | \n",
2186 | " 5 | \n",
2187 | " 2 | \n",
2188 | " 2 | \n",
2189 | " 9 | \n",
2190 | " 9 | \n",
2191 | " 3 | \n",
2192 | " 4 | \n",
2193 | "
\n",
2194 | " \n",
2195 | " | 95 | \n",
2196 | " 6 | \n",
2197 | " 6 | \n",
2198 | " 1 | \n",
2199 | " 0 | \n",
2200 | " 1 | \n",
2201 | " 8 | \n",
2202 | " 2 | \n",
2203 | " 2 | \n",
2204 | " 7 | \n",
2205 | " 4 | \n",
2206 | " ... | \n",
2207 | " 8 | \n",
2208 | " 8 | \n",
2209 | " 0 | \n",
2210 | " 5 | \n",
2211 | " 2 | \n",
2212 | " 2 | \n",
2213 | " 9 | \n",
2214 | " 9 | \n",
2215 | " 3 | \n",
2216 | " 4 | \n",
2217 | "
\n",
2218 | " \n",
2219 | " | 96 | \n",
2220 | " 6 | \n",
2221 | " 6 | \n",
2222 | " 1 | \n",
2223 | " 0 | \n",
2224 | " 1 | \n",
2225 | " 8 | \n",
2226 | " 2 | \n",
2227 | " 7 | \n",
2228 | " 7 | \n",
2229 | " 4 | \n",
2230 | " ... | \n",
2231 | " 8 | \n",
2232 | " 8 | \n",
2233 | " 1 | \n",
2234 | " 5 | \n",
2235 | " 2 | \n",
2236 | " 2 | \n",
2237 | " 9 | \n",
2238 | " 9 | \n",
2239 | " 3 | \n",
2240 | " 4 | \n",
2241 | "
\n",
2242 | " \n",
2243 | " | 97 | \n",
2244 | " 6 | \n",
2245 | " 6 | \n",
2246 | " 1 | \n",
2247 | " 0 | \n",
2248 | " 1 | \n",
2249 | " 8 | \n",
2250 | " 2 | \n",
2251 | " 2 | \n",
2252 | " 7 | \n",
2253 | " 4 | \n",
2254 | " ... | \n",
2255 | " 8 | \n",
2256 | " 8 | \n",
2257 | " 0 | \n",
2258 | " 5 | \n",
2259 | " 2 | \n",
2260 | " 2 | \n",
2261 | " 9 | \n",
2262 | " 9 | \n",
2263 | " 3 | \n",
2264 | " 4 | \n",
2265 | "
\n",
2266 | " \n",
2267 | " | 98 | \n",
2268 | " 6 | \n",
2269 | " 6 | \n",
2270 | " 1 | \n",
2271 | " 0 | \n",
2272 | " 1 | \n",
2273 | " 8 | \n",
2274 | " 2 | \n",
2275 | " 2 | \n",
2276 | " 7 | \n",
2277 | " 4 | \n",
2278 | " ... | \n",
2279 | " 8 | \n",
2280 | " 8 | \n",
2281 | " 0 | \n",
2282 | " 5 | \n",
2283 | " 2 | \n",
2284 | " 2 | \n",
2285 | " 9 | \n",
2286 | " 9 | \n",
2287 | " 3 | \n",
2288 | " 4 | \n",
2289 | "
\n",
2290 | " \n",
2291 | "
\n",
2292 | "
99 rows × 451 columns
\n",
2293 | "