diff --git a/sandbox/run_cobweb_multi.py b/sandbox/run_cobweb_multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..9ade3af5a848eeafa60a4d22ce16e02637bb40c2
--- /dev/null
+++ b/sandbox/run_cobweb_multi.py
@@ -0,0 +1,76 @@
+import gym
+from stable_baselines.common import make_vec_env
+from stable_baselines.common.policies import MlpPolicy
+from stable_baselines import PPO2
+import tutorenvs
+from tutorenvs.multicolumn import MultiColumnAdditionDigitsEnv
+from tutorenvs.multicolumn import MultiColumnAdditionSymbolic
+import numpy as np
+from pprint import pprint
+
+from concept_formation.cobweb3 import Cobweb3Tree
+from concept_formation.visualize import visualize
+
+from tutorenvs.utils import DataShopLogger
+
+def train_tree(n=10, logger=None):
+ tree = Cobweb3Tree()
+ env = MultiColumnAdditionSymbolic(logger=logger)
+
+ p = 0
+ nhints = 0
+ while p < n:
+ # make a copy of the state
+ state = {a: env.state[a] for a in env.state}
+ env.render()
+
+ concept = tree.categorize(state)
+ sel = concept.predict('selection')
+ inp = concept.predict('input')
+
+ if sel == "done":
+ act = 'ButtonPressed'
+ else:
+ act = "UpdateField"
+
+ sai = (sel, act, inp)
+
+ if sel is None or inp is None:
+ nhints += 1
+ sai = env.request_demo()
+ sai = (sai[0], sai[1], sai[2]['value'])
+
+ reward = env.apply_sai(sai[0], sai[1], {'value': sai[2]})
+ # print('reward', reward)
+
+ if reward < 0:
+ nhints += 1
+ sai = env.request_demo()
+ sai = (sai[0], sai[1], sai[2]['value'])
+ reward = env.apply_sai(sai[0], sai[1], {'value': sai[2]})
+
+ state['selection'] = sai[0]
+ state['input'] = str(sai[2])
+ tree.ifit(state)
+
+ if sai[0] == "done" and reward == 1.0:
+ print('# hints =', nhints)
+ nhints = 0
+ print("Problem %s of %s" % (p, n))
+ p += 1
+
+ return tree
+
+if __name__ == "__main__":
+
+ logger = DataShopLogger('MulticolumnAdditionTutor', extra_kcs=['field'])
+ for _ in range(1):
+ tree = train_tree(200, logger)
+ visualize(tree)
+
+ # env = MultiColumnAdditionSymbolic()
+
+ # while True:
+ # sai = env.request_demo()
+ # env.apply_sai(sai[0], sai[1], sai[2])
+ # env.render()
diff --git a/sandbox/run_decision_tree_multi-v1.py b/sandbox/run_decision_tree_multi-v1.py
index 225331a9bcdeb4e4068ba3e2a755fa61c13d09b1..bfe2d7d3706ea03245fae08e14b02f4bfc7fd811 100644
--- a/sandbox/run_decision_tree_multi-v1.py
+++ b/sandbox/run_decision_tree_multi-v1.py
@@ -8,54 +8,70 @@ from tutorenvs.multicolumn import MultiColumnAdditionSymbolic
import numpy as np
from sklearn.tree import DecisionTreeClassifier
+from sklearn.feature_extraction import DictVectorizer
-def train_tree(n=10):
+from tutorenvs.utils import DataShopLogger
+
+def train_tree(n=10, logger=None):
X = []
y = []
+ dv = DictVectorizer()
+ actions = []
+ action_mapping = {}
+ rev_action_mapping = {}
tree = DecisionTreeClassifier()
- env = MultiColumnAdditionSymbolic()
+ env = MultiColumnAdditionSymbolic(logger=logger)
p = 0
while p < n:
- state = env.get_rl_state()
+ # make a copy of the state
+ state = {a: env.state[a] for a in env.state}
env.render()
- try:
- response = decision_tree.predict(state)
- except:
- response = None
+ if rev_action_mapping == {}:
+ sai = None
+ else:
+ vstate = dv.transform([state])
+ sai = rev_action_mapping[tree.predict(vstate)[0]]
- if response is None:
+ if sai is None:
print('hint')
sai = env.request_demo()
+ sai = (sai[0], sai[1], sai[2]['value'])
- else:
- sai = (response['selection'],
- response['action'],
- response['inputs'])
-
- reward = env.apply_sai(sai[0], sai[1], sai[2])
+ reward = env.apply_sai(sai[0], sai[1], {'value': sai[2]})
print('reward', reward)
if reward < 0:
print('hint')
sai = env.request_demo()
- reward = env.apply_sai(sai[0], sai[1], sai[2])
+ sai = (sai[0], sai[1], sai[2]['value'])
+ reward = env.apply_sai(sai[0], sai[1], {'value': sai[2]})
X.append(state)
y.append(sai)
- if sai.selection == "done" and reward == 1.0:
+ Xv = dv.fit_transform(X)
+ actions = list(set(y))
+ action_mapping = {l: i for i, l in enumerate(actions)}
+ rev_action_mapping = {i: l for i, l in enumerate(actions)}
+ yv = [action_mapping[l] for l in y]
+
+ tree.fit(Xv, yv)
+
+ if sai[0] == "done" and reward == 1.0:
p += 1
return tree
if __name__ == "__main__":
- # tree = train_tree(10)
- env = MultiColumnAdditionSymbolic()
+ logger = DataShopLogger('MulticolumnAdditionTutor', extra_kcs=['field'])
+ for _ in range(10):
+ tree = train_tree(100, logger)
+ # env = MultiColumnAdditionSymbolic()
- while True:
- sai = env.request_demo()
- env.apply_sai(sai[0], sai[1], sai[2])
- env.render()
+ # while True:
+ # sai = env.request_demo()
+ # env.apply_sai(sai[0], sai[1], sai[2])
+ # env.render()
diff --git a/sandbox/run_dual_decision_tree_multi.py b/sandbox/run_dual_decision_tree_multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..21e5fed3b8dafce41b56ef44722f3aec19b27973
--- /dev/null
+++ b/sandbox/run_dual_decision_tree_multi.py
@@ -0,0 +1,100 @@
+import gym
+from stable_baselines.common import make_vec_env
+from stable_baselines.common.policies import MlpPolicy
+from stable_baselines import PPO2
+import tutorenvs
+from tutorenvs.multicolumn import MultiColumnAdditionDigitsEnv
+from tutorenvs.multicolumn import MultiColumnAdditionSymbolic
+import numpy as np
+
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.feature_extraction import DictVectorizer
+
+from tutorenvs.utils import DataShopLogger
+
+def train_tree(n=10, logger=None):
+ X = []
+ y_sel = []
+ y_inp = []
+ dv = DictVectorizer()
+ selections = []
+ selection_mapping = {}
+ rev_selection_mapping = {}
+ selection_tree = DecisionTreeClassifier()
+
+ inputs = []
+ input_mapping = {}
+ rev_input_mapping = {}
+ input_tree = DecisionTreeClassifier()
+
+ env = MultiColumnAdditionSymbolic(logger=logger)
+
+ p = 0
+ while p < n:
+ # make a copy of the state
+ state = {a: env.state[a] for a in env.state}
+ env.render()
+
+ if rev_selection_mapping == {}:
+ sai = None
+ else:
+ vstate = dv.transform([state])
+ sel = rev_selection_mapping[selection_tree.predict(vstate)[0]]
+ if sel == 'done':
+ act = 'ButtonPressed'
+ else:
+ act = "UpdateField"
+ inp = rev_input_mapping[input_tree.predict(vstate)[0]]
+ sai = (sel, act, inp)
+
+ if sai is None:
+ print('hint')
+ sai = env.request_demo()
+ sai = (sai[0], sai[1], sai[2]['value'])
+
+ reward = env.apply_sai(sai[0], sai[1], {'value': sai[2]})
+ print('reward', reward)
+
+ if reward < 0:
+ print('hint')
+ sai = env.request_demo()
+ sai = (sai[0], sai[1], sai[2]['value'])
+ reward = env.apply_sai(sai[0], sai[1], {'value': sai[2]})
+
+ X.append(state)
+ y_sel.append(sai[0])
+ y_inp.append(sai[2])
+
+ Xv = dv.fit_transform(X)
+
+ selections = list(set(y_sel))
+ selection_mapping = {l: i for i, l in enumerate(selections)}
+ rev_selection_mapping = {i: l for i, l in enumerate(selections)}
+
+ inputs = list(set(y_inp))
+ input_mapping = {l: i for i, l in enumerate(inputs)}
+ rev_input_mapping = {i: l for i, l in enumerate(inputs)}
+
+ yv_sel = [selection_mapping[l] for l in y_sel]
+ yv_inp = [input_mapping[l] for l in y_inp]
+
+ selection_tree.fit(Xv, yv_sel)
+ input_tree.fit(Xv, yv_inp)
+
+ if sai[0] == "done" and reward == 1.0:
+ print("Problem %s of %s" % (p, n))
+ p += 1
+
+ return selection_tree, input_tree
+
+if __name__ == "__main__":
+
+ logger = DataShopLogger('MulticolumnAdditionTutor', extra_kcs=['field'])
+ for _ in range(1):
+ tree = train_tree(1000, logger)
+ # env = MultiColumnAdditionSymbolic()
+
+ # while True:
+ # sai = env.request_demo()
+ # env.apply_sai(sai[0], sai[1], sai[2])
+ # env.render()
diff --git a/sandbox/run_single_decision_tree_multi.py b/sandbox/run_single_decision_tree_multi.py
new file mode 100644
index 0000000000000000000000000000000000000000..c360acd4d19376956841f97d3e140e2b23e95959
--- /dev/null
+++ b/sandbox/run_single_decision_tree_multi.py
@@ -0,0 +1,78 @@
+import gym
+from stable_baselines.common import make_vec_env
+from stable_baselines.common.policies import MlpPolicy
+from stable_baselines import PPO2
+import tutorenvs
+from tutorenvs.multicolumn import MultiColumnAdditionDigitsEnv
+from tutorenvs.multicolumn import MultiColumnAdditionSymbolic
+import numpy as np
+
+from sklearn.tree import DecisionTreeClassifier
+from sklearn.feature_extraction import DictVectorizer
+
+from tutorenvs.utils import DataShopLogger
+
+def train_tree(n=10, logger=None):
+ X = []
+ y = []
+ dv = DictVectorizer()
+ actions = []
+ action_mapping = {}
+ rev_action_mapping = {}
+ selection_tree = DecisionTreeClassifier()
+ input_tree = DecisionTreeClassifier()
+ env = MultiColumnAdditionSymbolic(logger=logger)
+
+ p = 0
+ while p < n:
+ # make a copy of the state
+ state = {a: env.state[a] for a in env.state}
+ env.render()
+
+ if rev_action_mapping == {}:
+ sai = None
+ else:
+ vstate = dv.transform([state])
+ sai = rev_action_mapping[tree.predict(vstate)[0]]
+
+ if sai is None:
+ print('hint')
+ sai = env.request_demo()
+ sai = (sai[0], sai[1], sai[2]['value'])
+
+ reward = env.apply_sai(sai[0], sai[1], {'value': sai[2]})
+ print('reward', reward)
+
+ if reward < 0:
+ print('hint')
+ sai = env.request_demo()
+ sai = (sai[0], sai[1], sai[2]['value'])
+ reward = env.apply_sai(sai[0], sai[1], {'value': sai[2]})
+
+ X.append(state)
+ y.append(sai)
+
+ Xv = dv.fit_transform(X)
+ actions = set(y)
+ action_mapping = {l: i for i, l in enumerate(actions)}
+ rev_action_mapping = {i: l for i, l in enumerate(actions)}
+ yv = [action_mapping[l] for l in y]
+
+ tree.fit(Xv, yv)
+
+ if sai[0] == "done" and reward == 1.0:
+ p += 1
+
+ return tree
+
+if __name__ == "__main__":
+
+ logger = DataShopLogger('MulticolumnAdditionTutor', extra_kcs=['field'])
+ for _ in range(10):
+ tree = train_tree(100, logger)
+ # env = MultiColumnAdditionSymbolic()
+
+ # while True:
+ # sai = env.request_demo()
+ # env.apply_sai(sai[0], sai[1], sai[2])
+ # env.render()
diff --git a/tutorenvs/multicolumn.py b/tutorenvs/multicolumn.py
index b3c7e848adfe8e2dd57964cdf9b63bca210f8ec9..c5e70d4600d677d1ed6b9a7352f8692ffdd63560 100644
--- a/tutorenvs/multicolumn.py
+++ b/tutorenvs/multicolumn.py
@@ -12,6 +12,7 @@ import numpy as np
from PIL import Image, ImageDraw
from tutorenvs.utils import BaseOppEnv
+from tutorenvs.utils import DataShopLogger
def custom_add(a, b):
if a == '':
@@ -22,10 +23,16 @@ def custom_add(a, b):
class MultiColumnAdditionSymbolic:
- def __init__(self):
+ def __init__(self, logger=None):
"""
Creates a state and sets a random problem.
"""
+ if logger is None:
+ print("CREATING LOGGER")
+ self.logger = DataShopLogger('MulticolumnAdditionTutor', extra_kcs=['field'])
+ else:
+ self.logger = logger
+ self.logger.set_student()
self.set_random_problem()
# self.reset("", "", "", "", "")
@@ -87,6 +94,7 @@ class MultiColumnAdditionSymbolic:
self.num_correct_steps = 0
self.num_incorrect_steps = 0
+ self.num_hints = 0
self.state = {
'hundreds_carry': '',
@@ -190,6 +198,7 @@ class MultiColumnAdditionSymbolic:
if add_counts:
d.text((0, 0), str(self.num_incorrect_steps), fill="red")
d.text((0, 10), str(self.num_correct_steps), fill="green")
+ d.text((0, 20), str(self.num_hints), fill="blue")
if add_dot:
d.ellipse((add_dot[0]-3, add_dot[1]-3, add_dot[0]+3, add_dot[1]+3),
@@ -225,9 +234,12 @@ class MultiColumnAdditionSymbolic:
return state_output
def set_random_problem(self):
- upper = str(randint(1,999))
- lower = str(randint(1,999))
+ # upper = str(randint(1,999))
+ # lower = str(randint(1,999))
+ upper = str(randint(1,9))
+ lower = str(randint(1,9))
self.reset(upper=upper, lower=lower)
+ self.logger.set_problem("%s_%s" % (upper, lower))
def apply_sai(self, selection, action, inputs):
"""
@@ -236,10 +248,14 @@ class MultiColumnAdditionSymbolic:
reward = self.evaluate_sai(selection, action, inputs)
if reward > 0:
+ outcome = "CORRECT"
self.num_correct_steps += 1
else:
+ outcome = "INCORRECT"
self.num_incorrect_steps += 1
+ self.logger.log_step(selection, action, inputs['value'], outcome, [selection])
+
if reward == -1.0:
return reward
@@ -352,6 +368,15 @@ class MultiColumnAdditionSymbolic:
# TODO still need to rewrite for multi column arith
def request_demo(self):
+ demo = self.get_demo()
+ feedback_text = "selection: %s, action: %s, input: %s" % (demo[0],
+ demo[1], demo[2]['value'])
+ self.logger.log_hint(feedback_text, [demo[0]])
+ self.num_hints += 1
+
+ return demo
+
+ def get_demo(self):
"""
Returns a correct next-step SAI
"""