Skip to content
GitLab
Projects
Groups
Snippets
Help
Loading...
Help
Help
Support
Community forum
Keyboard shortcuts
?
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
2
20200503_GFZ
Project overview
Project overview
Details
Activity
Releases
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Issues
0
Issues
0
List
Boards
Labels
Service Desk
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Operations
Operations
Incidents
Environments
Packages & Registries
Packages & Registries
Package Registry
Analytics
Analytics
CI / CD
Repository
Value Stream
Wiki
Wiki
Snippets
Snippets
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
ML@GFZ
hackathons
20200503_GFZ
Commits
911379a0
Commit
911379a0
authored
Mar 05, 2020
by
Daniel Berger
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Python linear model and neural network
parent
133d5cff
Changes
3
Expand all
Hide whitespace changes
Inline
Side-by-side
Showing
3 changed files
with
8508 additions
and
0 deletions
+8508
-0
group5/LUCAS.csv
group5/LUCAS.csv
+8328
-0
group5/Neural Network.py
group5/Neural Network.py
+88
-0
group5/Neural_Network_with_linear_model.py
group5/Neural_Network_with_linear_model.py
+92
-0
No files found.
group5/LUCAS.csv
0 → 100644
View file @
911379a0
This diff is collapsed.
Click to expand it.
group5/Neural Network.py
0 → 100644
View file @
911379a0
#!/usr/bin/env python3
import
pandas
as
pd
import
numpy
as
np
import
matplotlib.pyplot
as
plt
import
csv
from
datetime
import
datetime
,
timedelta
from
keras.models
import
Sequential
from
keras.layers
import
Dense
,
Dropout
,
BatchNormalization
,
Conv2D
from
keras
import
optimizers
from
keras
import
backend
as
K
from
sklearn.model_selection
import
train_test_split
import
sklearn
from
sklearn.metrics
import
mean_squared_error
from
sklearn.preprocessing
import
MinMaxScaler
import
os
import
sys
df
=
pd
.
read_csv
(
"../../data/LUCAS_full/LUCAS.csv"
,
header
=
0
)
columns
=
[
"SOC"
,
"clay"
,
"CaCO3"
]
out_data
=
df
[
columns
].
values
input_data
=
df
[
df
.
columns
[
4
:]].
values
def
is_window
(
column
):
without_x
=
column
[
1
:]
x
=
int
(
without_x
)
if
x
>
1350
and
x
<
1450
:
print
(
x
)
return
False
if
x
>
1850
and
x
<
2050
:
print
(
x
)
return
False
return
True
input_data
=
df
[
df
.
columns
[
4
:]]
#input_data = input_data[[c for c in input_data.columns if is_window(c)]]
input_data
=
input_data
.
values
max_values
=
np
.
max
(
input_data
,
axis
=
1
)
#for idx, max_value in enumerate(max_values):
# input_data[idx] = input_data[idx] / max_value
# input_train = input_data[:int(0.7*len(input_data))]
# input_test = input_data[int(0.7*len(input_data)):]
# output_train = out_data[:int(0.7*len(out_data))]
# output_test = out_data[int(0.7*len(out_data)):]
input_train
,
input_test
,
output_train
,
output_test
=
train_test_split
(
input_data
,
out_data
,
test_size
=
0.3
,
shuffle
=
True
)
K
.
clear_session
()
model
=
Sequential
()
model
.
add
(
BatchNormalization
(
input_shape
=
(
input_train
.
shape
[
1
],)))
model
.
add
(
Dense
(
64
,
activation
=
"selu"
))
model
.
add
(
Dense
(
128
,
activation
=
"selu"
))
model
.
add
(
Dense
(
3
,
activation
=
"selu"
))
#model.compile(optimizer="RMSprop", loss="mean_squared_error")
model
.
compile
(
optimizer
=
"adam"
,
loss
=
"mean_squared_error"
)
model
.
fit
(
input_train
,
output_train
,
epochs
=
200
,
batch_size
=
32
)
calculated_cal
=
model
.
predict
(
input_train
)
calculated_val
=
model
.
predict
(
input_test
)
for
i
in
range
(
len
(
columns
)):
rmse_train
=
np
.
sqrt
(
mean_squared_error
(
output_train
[:,
i
],
calculated_cal
[:,
i
]))
rmse_val
=
np
.
sqrt
(
mean_squared_error
(
output_test
[:,
i
],
calculated_val
[:,
i
]))
R2_train
=
sklearn
.
metrics
.
r2_score
(
output_train
[:,
i
],
calculated_cal
[:,
i
])
R2_val
=
sklearn
.
metrics
.
r2_score
(
output_test
[:,
i
],
calculated_val
[:,
i
])
print
(
"Overall:"
)
print
(
columns
[
i
])
print
(
R2_train
)
print
(
R2_val
)
import
pdb
pdb
.
set_trace
()
group5/Neural_Network_with_linear_model.py
0 → 100644
View file @
911379a0
#!/usr/bin/env python3
import
pandas
as
pd
import
numpy
as
np
import
matplotlib.pyplot
as
plt
import
csv
from
datetime
import
datetime
,
timedelta
from
keras.models
import
Sequential
from
keras.layers
import
Dense
,
Dropout
,
BatchNormalization
,
Conv2D
from
keras
import
optimizers
from
keras
import
backend
as
K
from
sklearn.model_selection
import
train_test_split
from
sklearn.linear_model
import
LinearRegression
import
sklearn
from
sklearn.metrics
import
mean_squared_error
from
sklearn.preprocessing
import
MinMaxScaler
import
os
import
sys
df
=
pd
.
read_csv
(
"../../data/LUCAS_full/LUCAS.csv"
,
header
=
0
)
columns
=
[
"SOC"
,
"clay"
,
"CaCO3"
]
out_data
=
df
[
columns
].
values
input_data
=
df
[
df
.
columns
[
4
:]].
values
def
is_window
(
column
):
without_x
=
column
[
1
:]
x
=
int
(
without_x
)
if
x
>
1350
and
x
<
1450
:
print
(
x
)
return
False
if
x
>
1850
and
x
<
2050
:
print
(
x
)
return
False
return
True
input_data
=
df
[
df
.
columns
[
4
:]]
#input_data = input_data[[c for c in input_data.columns if is_window(c)]]
input_data
=
input_data
.
values
#for idx, max_value in enumerate(max_values):
# input_data[idx] = input_data[idx] / max_value
# input_train = input_data[:int(0.7*len(input_data))]
# input_test = input_data[int(0.7*len(input_data)):]
# output_train = out_data[:int(0.7*len(out_data))]
# output_test = out_data[int(0.7*len(out_data)):]
input_train
,
input_test
,
output_train
,
output_test
=
train_test_split
(
input_data
,
out_data
,
test_size
=
0.3
,
shuffle
=
True
)
reg
=
LinearRegression
().
fit
(
input_train
,
output_train
)
reg_output
=
reg
.
predict
(
input_train
)
ressiduals
=
reg_output
-
output_train
K
.
clear_session
()
model
=
Sequential
()
model
.
add
(
BatchNormalization
(
input_shape
=
(
input_train
.
shape
[
1
],)))
model
.
add
(
Dense
(
64
,
activation
=
"selu"
))
model
.
add
(
Dense
(
128
,
activation
=
"selu"
))
model
.
add
(
Dense
(
3
,
activation
=
"selu"
))
#model.compile(optimizer="RMSprop", loss="mean_squared_error")
model
.
compile
(
optimizer
=
"adam"
,
loss
=
"mean_squared_error"
)
model
.
fit
(
input_train
,
ressiduals
,
epochs
=
200
,
batch_size
=
32
)
calculated_cal
=
model
.
predict
(
input_train
)
+
reg
.
predict
(
input_train
)
calculated_val
=
model
.
predict
(
input_test
)
+
reg
.
predict
(
input_test
)
for
i
in
range
(
len
(
columns
)):
rmse_train
=
np
.
sqrt
(
mean_squared_error
(
output_train
[:,
i
],
calculated_cal
[:,
i
]))
rmse_val
=
np
.
sqrt
(
mean_squared_error
(
output_test
[:,
i
],
calculated_val
[:,
i
]))
R2_train
=
sklearn
.
metrics
.
r2_score
(
output_train
[:,
i
],
calculated_cal
[:,
i
])
R2_val
=
sklearn
.
metrics
.
r2_score
(
output_test
[:,
i
],
calculated_val
[:,
i
])
print
(
"Overall:"
)
print
(
columns
[
i
])
print
(
R2_train
)
print
(
R2_val
)
import
pdb
pdb
.
set_trace
()
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
.
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment