class Market():
"""General interface for calling the different
market mechanisms
Parameters
----------
bm: BidManager
All bids are stored in the bid manager
transactions: TransactionManager
The set of all tranasactions in the Market.
This argument get updated after the market ran.
extra: dict
Extra information provided by the mechanisms.
Gets updated after an execution of the run.
Returns
-------
Examples
---------
If everyone is buying, the transaction
dataframe is returned empty as well as the extra
dictionary.
>>> mar = pm.Market()
>>> mar.accept_bid(1, 2, 0, True)
0
>>> mar.accept_bid(2, 3, 1, True)
1
>>> trans, extra = mar.run('huang')
>>> extra
{}
>>> trans.get_df()
Empty DataFrame
Columns: [bid, quantity, price, source, active]
Index: []
If everyone is buying, the transaction
dataframe is returned empty as well as the extra
dictionary.
>>> mar = pm.Market()
>>> mar.accept_bid(1, 2, 0, False)
0
>>> mar.accept_bid(2, 3, 1, False)
1
>>> trans, extra = mar.run('huang')
>>> extra
{}
>>> trans.get_df()
Empty DataFrame
Columns: [bid, quantity, price, source, active]
Index: []
A very simple auction where nobody trades
>>> mar = pm.Market()
>>> mar.accept_bid(1, 3, 0, True)
0
>>> mar.accept_bid(1, 2, 1, False)
1
>>> trans, extra = mar.run('huang')
>>> extra
{'price_sell': 2.0, 'price_buy': 3.0, 'quantity_traded': 0}
>>> trans.get_df()
Empty DataFrame
Columns: [bid, quantity, price, source, active]
Index: []
"""
def __init__(self):
"""TODO: to be defined1."""
self.bm = BidManager()
self.transactions = TransactionManager()
self.extra = {}
def accept_bid(self, *args):
"""Adds a bid to the bid manager
Parameters
----------
*args :
List of parameters requried to create a bid.
See `BidManager` documentation.
Returns
-------
bid_id: int
The id of the new created bid in the BidManger
"""
bid_id = self.bm.add_bid(*args)
return bid_id
def run(self, algo, *args, **kwargs):
"""Runs a given mechanism with the current
bids
Parameters
----------
algo : str
One of:
* 'p2p'
* 'huang'
* 'muda'
*args :
Extra arguments to pass to the algorithm.
**kwargs :
Extra keyworded arguments to pass to the algorithm
Returns
-------
transactions: TransactionManager
The transaction manager holding all the transactions
returned by the mechanism.
extra: dict
Dictionary with extra information returned by the
executed method.
"""
df = self.bm.get_df()
mec = MECHANISM[algo](df, *args, **kwargs)
transactions, extra = mec.run()
self.transactions = transactions
self.extra = extra
return transactions, extra
def statistics(self, reservation_prices=None, exclude=[]):
"""Computes the standard statistics of the market
Parameters
----------
reservation_prices (dict, optional) :
the reservation prices of the users. If there is none,
the bid will be assumed truthfull
reservation_prices :
(Default value = None)
exclude :
List of mechanisms to ignore will comuting statistics
Returns
-------
stats : dict
Dictionary with the differnt statistics. Currently:
* percentage_welfare
* percentage_traded
* profits
"""
stats = {}
extras = {}
if 'fees' in self.extra:
extras['fees'] = self.extra['fees']
extras['reservation_prices'] = reservation_prices
for stat in STATS:
if stat not in exclude:
stats[stat] = STATS[stat](self.bm.get_df(),
self.transactions.get_df(), **extras)
self.stats = stats
return stats
def plot(self):
"""Plots both demand curves"""
df = self.bm.get_df()
plot_demand_curves(df)
def plot_method(self, method, ax=None):
"""
Plots a figure specific for a given method,
reflecting the main characteristics of its solution.
It requires that the algorithm has run before.
Parameters
----------
method : str
One of `p2p`, `muda`, `huang`
ax :
(Default value = None)
Returns
-------
"""
trans = self.transactions
bids = self.bm
e = self.extra
if method == 'p2p':
ax = plot_trades_as_graph(bids, trans, ax)
elif method == 'muda':
try:
left_players = e['left']
right_players = e['right']
left_price = e['price_left']
right_price = e['price_right']
ax = plot_both_side_muda(bids, left_players, right_players,
left_price, right_price)
except KeyError as e:
print('Some of the parameters required were not found',
'Make sure that the algorithm executed correctly.')
elif method == 'huang':
try:
price_sell = e['price_sell']
price_buy = e['price_buy']
quantity_traded = e['quantity_traded']
ax = plot_huang_auction(bids, price_sell, price_buy,
quantity_traded, ax)
except KeyError as e:
print('Some of the parameters required were not found',
'Make sure that the algorithm executed correctly.')
return ax
def __init__(self):
"""TODO: to be defined1."""
self.bm = BidManager()
self.transactions = TransactionManager()
self.extra = {}
def bid_dataset_3():
bm = BidManager()
bm.add_bid(1 , 6 , 0 , True , 0)
bm.add_bid(0.1 , 5 , 1 , True , 0)
bm.add_bid(1.9 , 4 , 2 , True , 0)
bm.add_bid(1 , 3 , 3 , True , 0)
bm.add_bid(1 , 1 , 4 , False , 0)
bm.add_bid(1.5 , 2 , 5 , False , 0)
bm.add_bid(1 , 2.5 , 6 , False , 0)
bm.add_bid(1 , 4 , 7 , False , 0)
return bm.get_df()
def bid_dataset_muda_example_1():
"""Simple example with 3 biders as described
in the MUDA paper
Returns
-------
df : pandas.DataFrame
dataframe with all the bids
"""
bm = BidManager()
bm.add_bid(1, 100, 0, True, 0)
bm.add_bid(1, 90, 0, True, 0)
bm.add_bid(1, 80, 0, True, 0)
bm.add_bid(1, 60, 0, True, 0)
bm.add_bid(1, 40, 0, True, 0)
bm.add_bid(1, 20, 0, True, 0)
bm.add_bid(1, 10, 1, False, 0)
bm.add_bid(1, 20, 1, False, 0)
bm.add_bid(1, 40, 1, False, 0)
bm.add_bid(1, 60, 1, False, 0)
bm.add_bid(1, 70, 1, False, 0)
bm.add_bid(1, 15, 2, False, 0)
bm.add_bid(1, 25, 2, False, 0)
bm.add_bid(1, 35, 2, False, 0)
bm.add_bid(1, 45, 2, False, 0)
bm.add_bid(1, 65, 2, False, 0)
return bm.get_df()
def bid_dataset_0():
"""
Simple bid dataset in which demand intersects supply
"""
bm = BidManager()
bm.add_bid(1, 3, 0, True, 0)
bm.add_bid(2, 4, 1, True, 0)
bm.add_bid(5, 1, 2, True, 0)
bm.add_bid(4, 2, 3, False, 0)
bm.add_bid(1, 1, 4, False, 0)
bm.add_bid(5, 6, 5, False, 0)
return bm.get_df()
def bid_dataset_1():
"""
Larger test case, supply intersects demand
"""
bm = BidManager()
bm.add_bid(1, 6.7, 0, True, 0)
bm.add_bid(1, 6.6, 1, True, 0)
bm.add_bid(1, 6.5, 2, True, 0)
bm.add_bid(1, 6.4, 3, True, 0)
bm.add_bid(1, 6.3, 4, True, 0)
bm.add_bid(1, 6, 5, True, 0)
bm.add_bid(1, 1, 6, False, 0)
bm.add_bid(1, 2, 7, False, 0)
bm.add_bid(2, 3, 8, False, 0)
bm.add_bid(2, 4, 9, False, 0)
bm.add_bid(1, 6.1, 10, False, 0)
return bm.get_df()
def test_merge_same_price():
""" Test the merge_same_price
functionalty with repeated
price in both sides and equal
price for the repated price.
"""
bm = BidManager()
bm.add_bid(1, 100, 0, True, 0)
bm.add_bid(3, 100, 1, True, 0)
bm.add_bid(2.3, 85, 2, True, 0)
bm.add_bid(2.1, 90, 7, True, 0)
bm.add_bid(0.4, 90, 8, True, 0)
bm.add_bid(0.5, 90, 4, False, 0)
bm.add_bid(4.2, 1, 5, False, 0)
bm.add_bid(0.1, 90, 6, False, 0)
df = bm.get_df()
df_new, maping = merge_same_price(df)
X_original = np.array([
[2.3, 85, 2, True, 0, True],
[4.2, 1, 5, False, 0, True],
[2.5, 90, 9, True, 0, True],
[4, 100, 10, True, 0, True],
[0.6, 90, 11, False, 0, True],
]).astype(float)
maping_original = {
0: [2], 1: [3, 4], 2: [0, 1], 3: [6], 4: [5, 7]
}
X_new = df_new.sort_values('user').values.astype(float)
assert np.allclose(X_original, X_new)
for k in maping_original:
assert maping_original[k] == maping[k]